Mercurial > hg > mirex-meta-analysis
changeset 1:818a4b5f3384
Initial version, includes all files
Deleted previous stuff which was junk
| author | Jordan Smith <jordan.smith@eecs.qmul.ac.uk> |
|---|---|
| date | Fri, 20 Sep 2013 16:36:45 +0100 |
| parents | 89cf35646cb9 |
| children | 624231da830b |
| files | 1-get_mirex_estimates.rb 2-generate_smith2013_ismir.m MIREX_data/.DS_Store MIREX_data/2009/structure.anon_1.csv MIREX_data/2009/structure.anon_2.csv MIREX_data/2009/structure.mauch.csv MIREX_data/2009/structure.paulus.csv MIREX_data/2009/structure.peeters.csv MIREX_data/2010/2009data/BV1-per_track_results.csv MIREX_data/2010/2009data/BV2-per_track_results.csv MIREX_data/2010/2009data/GP7-per_track_results.csv MIREX_data/2010/2009data/MHRAF2-per_track_results.csv MIREX_data/2010/2009data/MND1-per_track_results.csv MIREX_data/2010/2009data/WB1-per_track_results.csv MIREX_data/2010/2010data/BV1-per_track_results.csv MIREX_data/2010/2010data/BV2-per_track_results.csv MIREX_data/2010/2010data/GP7-per_track_results.csv MIREX_data/2010/2010data/MHRAF2-per_track_results.csv MIREX_data/2010/2010data/MND1-per_track_results.csv MIREX_data/2010/2010data/WB1-per_track_results.csv MIREX_data/2010/2010data2/BV1-per_track_results.csv MIREX_data/2010/2010data2/BV2-per_track_results.csv MIREX_data/2010/2010data2/GP7-per_track_results.csv MIREX_data/2010/2010data2/MHRAF2-per_track_results.csv MIREX_data/2010/2010data2/MND1-per_track_results.csv MIREX_data/2010/2010data2/WB1-per_track_results.csv MIREX_data/2011/2009data/CL1-per_track_results.csv MIREX_data/2011/2009data/GP6-per_track_results.csv MIREX_data/2011/2009data/MHRAF1-per_track_results.csv MIREX_data/2011/2009data/MHRAF2-per_track_results.csv MIREX_data/2011/2009data/MHRAF3-per_track_results.csv MIREX_data/2011/2009data/SBVRS1-per_track_results.csv MIREX_data/2011/2010data/CL1-per_track_results.csv MIREX_data/2011/2010data/GP6-per_track_results.csv MIREX_data/2011/2010data/MHRAF1-per_track_results.csv MIREX_data/2011/2010data/MHRAF2-per_track_results.csv MIREX_data/2011/2010data/MHRAF3-per_track_results.csv MIREX_data/2011/2010data/SBVRS1-per_track_results.csv MIREX_data/2012/KSP1-per_track_results.csv MIREX_data/2012/KSP2-per_track_results.csv MIREX_data/2012/KSP3-per_track_results.csv MIREX_data/2012/MHRAF1-per_track_results.csv MIREX_data/2012/OYZS1-per_track_results.csv MIREX_data/2012/SBV1-per_track_results.csv MIREX_data/2012/SMGA1-per_track_results.csv MIREX_data/2012/SMGA2-per_track_results.csv MIREX_data/2012/SP1-per_track_results.csv MIREX_data/2012/per_track_results.csv collect_all_mirex_algo_output_data.m collect_all_mirex_annotations.m collect_all_mirex_results.m collect_all_public_annotations.m compile_datacubes.m do_correlation3.m do_correlation3_fig3_only.m do_correlation_analyses.m get_mirex_data.m match_mirex_to_public_data.m match_mirex_to_public_data_macro.m readme.txt script.m |
| diffstat | 61 files changed, 1799 insertions(+), 16417 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/1-get_mirex_estimates.rb Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,110 @@ +require "CSV" +require "open-uri" +# require "simplexml" +mirex_path = "/Users/jordan/Desktop/MIREX_data" # EDIT THIS TO BE YOUR OWN DESIRED PATH + +def url_download(uri, filename=".") + tmp = File.open(filename,'w') + tmptxt = [] + open(uri) {|f| + f.each_line {|line| tmptxt.push(line)} + } + tmp.write(tmptxt) + tmp.close +end + +def convert_file(filename) + ann_out_file = filename[0..-4] + "_gt.txt" + alg_out_file = filename[0..-4] + "_pred.txt" + ann_out = File.open(ann_out_file,'w') + alg_out = File.open(alg_out_file,'w') + text = File.open(filename,'r').readlines[1..-4].join("").split(/[\[\]]/) + ann = text[1] + alg = text[3] + ann_out.write(json_2_text(ann)) + alg_out.write(json_2_text(alg)) + ann_out.close + alg_out.close +end + +def json_2_text(json) + txt = [] + json = json.split("\n") + json.each do |line| + els = line.split(",") + # Make a LAB-style annotation (3-column): + # txt.push([els[0].split(" ")[-1].to_f, els[1].split(" ")[-1].to_f, els[2].split("\"")[-1]].join("\t")) + # Make a TXT-style annotation (2-column): + txt.push([els[0].split(" ")[-1].to_f, els[2].split("\"")[-1]].join("\t")) + end + txt.push([json[-1].split(",")[1].split(" ")[-1].to_f, "End"].join("\t")) + return txt.join("\n") +end + + +# # # # PART 1: DOWNLOAD ALL THE STRUCTURAL ANALYSIS EVALUTION DATA PUBLISHED BY MIREX + +# Define list of algorithms and datasets: +algos = ["SP1", "SMGA2", "MHRAF1", "SMGA1", "SBV1", "KSP2", "OYZS1", "KSP3", "KSP1"] +datasets = ["mrx09", "mrx10_1", "mrx10_2", "sal"] + +# Create appropriate directory tree and download CSV files: +puts("Downloading CSV files...\n") +datasets.each do |dset| + # Make dataset directory: + dir_path = File.join(mirex_path,dset) + Dir.mkdir(dir_path) unless File.directory?(dir_path) + algos.each do |algo| + # Make algorithm directory: + algo_path = File.join(mirex_path,dset,algo) + Dir.mkdir(algo_path) unless File.directory?(algo_path) + # Download the CSV file to this directory: + algocsvpath = File.join(mirex_path,dset,algo,"per_track_results.csv") + csv_path = File.join("http://nema.lis.illinois.edu/nema_out/mirex2012/results/struct",dset,algo,"per_track_results.csv") + url_download(csv_path, algocsvpath) + end +end + +# Read each CSV file and download all the json files it points to: +datasets.each do |dset| + algos.each do |algo| + puts( "Starting to download "+dset+ " dataset for " + algo + " algorithm...\n") + algocsvpath = File.join(mirex_path,dset,algo,"per_track_results.csv") + csv_data = CSV.read(algocsvpath) + header = csv_data.delete_at(0) + download_folder = File.join(mirex_path,dset,algo) + # For each line in the spreadsheet, extract the songid and download the corresponding json document. + csv_data.each do |line| + song_id = line[1] + url = "http://nema.lis.illinois.edu/nema_out/mirex2012/results/struct/" + dset + "/" + algo.downcase + "segments" + song_id.delete("_") + ".js" + download_path = File.join(download_folder,song_id + ".js") + # download_path = download_folder + "/" + song_id + ".js" + url_download(url, download_path) + end + end + puts("Done with " + dset + " dataset!\n") +end + +# Scan for all the json files, and convert each one into two text files, one for the algorithm output, one for the annotation: +all_json_files = Dir.glob(File.join(mirex_path,"*","*","*.js")) +all_json_files.each do |file| + convert_file(file) +end + + + +# # # # PART 2: GET (AND CONVERT) THE ANNOTATION DATA PUBLISHED BY OTHERS + +# Download and unzip all public annotations +list_of_db_urls = ["http://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/AIST.RWC-MDB-P-2001.CHORUS.zip", "http://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/AIST.RWC-MDB-C-2001.CHORUS.zip", "http://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/AIST.RWC-MDB-J-2001.CHORUS.zip", "http://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/AIST.RWC-MDB-G-2001.CHORUS.zip", "http://www.music.mcgill.ca/~jordan/salami/releases/SALAMI_data_v1.2.zip", "http://www.ifs.tuwien.ac.at/mir/audiosegmentation/dl/ep_groundtruth_excl_Paulus.zip", "http://musicdata.gforge.inria.fr/IRISA.RWC-MDB-P-2012.SEMLAB_v003_full.zip", "http://musicdata.gforge.inria.fr/IRISA.RWC-MDB-P-2012.SEMLAB_v003_reduced.zip", "http://musicdata.gforge.inria.fr/IRISA.RWC-MDB-P-2001.BLOCKS_v001.zip", "http://www.isophonics.net/files/annotations/The%20Beatles%20Annotations.tar.gz", "http://www.isophonics.net/files/annotations/Carole%20King%20Annotations.tar.gz", "http://www.isophonics.net/files/annotations/Queen%20Annotations.tar.gz", "http://www.isophonics.net/files/annotations/Michael%20Jackson%20Annotations.tar.gz", "http://www.isophonics.net/files/annotations/Zweieck%20Annotations.tar.gz", "http://www.cs.tut.fi/sgn/arg/paulus/beatles_sections_TUT.zip", "http://www.iua.upf.edu/~perfe/annotations/sections/beatles/structure_Beatles.rar"] + +public_data_path = File.join(mirex_path,"public_data") +Dir.mkdir(public_data_path) unless File.directory?(public_data_path) +list_of_db_urls.each do |db_url| + open(File.join(public_data_path,File.basename(db_url)), 'wb') do |foo| + foo.print open(db_url).read + end +end + +# # # # NOW, PLEASE EXIT THE SCRIPT, AND UNZIP ALL THOSE PACKAGES. +# # # # WHEN YOU'RE DONE, GO ONTO THE PARENT MATLAB FILE TO RUN THE ANALYSES. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/2-generate_smith2013_ismir.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,79 @@ +% This is a script that reproduces the results of the following ISMIR paper: +% +% Smith, J. B. L., and E. Chew. 2013. A meta-analysis of the MIREX Structural +% Segmentation task. Proceedings of the International Society for Music +% Information Retrieval Conference. Curitiba, Brazil. 251–6. +% +% DO read the README.txt before running this file. Among other things, it will +% explain that you must download some data and place it in the correct file +% tree before this script will work. +% +% DO read the comments in this script before running it. You may want to run +% it piece by piece in order to understand exactly what it is doing. + + +%% +% STEP 0: Set up parameters. +% Name the MIREX datasets and algorithms desired. +dsets = {'mrx09','mrx10_1','mrx10_2','sal'}; +algos = {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'}; +base_directory = '/Users/jordan/Documents/classes/mirex_data/2012'; +base_directory = '/Users/jordan/Desktop/MIREX_data'; +% You should get a copy of the evalution scripts in the Code.SoundSoftware +% repository. Again, please see the README... +addpath('/Users/jordan/Documents/structural_analysis_evaluation') + + +%% +% STEP 1: Download data from MIREX website: +% +% - Ground truth files +% - Algorithm output +% - Reported evaluation results +% (See README.txt to see where to get this data.) + + +%% +% STEP 2: Import all this data into some Matlab structures. +% +% 1. Assemble MIREX ground truth file data in Matlab. +[mirex_truth mirex_dset_origin] = collect_all_mirex_annotations(base_directory, dsets, algos); +% 2. Assemble MIREX algorithm output data in Matlab. +mirex_output = collect_all_mirex_algo_output_data(base_directory, dsets, algos); +% 3. Assemble MIREX evaluation results in Matlab. +mirex_results = collect_all_mirex_results(base_directory, dsets, algos); +% 4. Download public repositories of annotations. +% NB: You must do this manually, as per the README. +% 5. Assemble public ground truth data in Matlab. +[public_truth public_dset_origin] = collect_all_public_annotations(base_directory); + + +%% +% STEP 3: Match MIREX and public data. +% +% With this information, we can now easily search for matches between +% MIREX and public ground truth. +[pub2mir, mir2pub, P] = match_mirex_to_public_data(mirex_truth, public_truth, mirex_dset_origin, public_dset_origin); +% If you have already done this, do not repeat this time-consuming step. Instead: +% load match_mirex_to_public_data_results + +%% +% STEP 4: Compile datacubes +% 1. Compute extra evaluation measures using MIREX algorithm output. +% 2. Compute extra features of the annotations (song length, mean segment length, etc.). +% 3. Put it all together in a giant MEGADATACUBE. +[datacube newcube extracube indexing_info] = compile_datacubes(mirex_truth, ... + mirex_dset_origin, public_truth, mirex_output, mirex_results, mir2pub); +% If you have already done this, do not repeat this time-consuming step. Instead: +% load datacubes +megadatacube = [datacube newcube extracube]; + + +%% +% Step 5: Do the statistics! +% 1. Compute correlations between all these parameters. +% 2. Display correlation figures. +% 3. Display analysis result figure. +do_correlation_analyses % this one is just a script because it does not return any values. + +% You are now finished! \ No newline at end of file
--- a/MIREX_data/2009/structure.anon_1.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,299 +0,0 @@ -*fileID,overSegScore,underSegScore,pwF,pwPrecision,pwRecall,R,Fmeasure@0.5s,precRate@0.5s,recRate@0.5s,medianTrue2claim@0.5s,medianClaim2true@0.5s,Fmeasure@3s,precRate@3s,recRate@3s,medianTrue2claim@3s,medianClaim2true@3s -1,0.5564,0.64608,0.63605,0.70278,0.58089,0.75732,0,0,0,3.3504,4.8275,0.4,0.4,0.4,3.3504,4.8275 -2,0.57465,0.54137,0.50839,0.49608,0.52132,0.72671,0.2069,0.15789,0.3,0.99745,6.4359,0.41379,0.31579,0.6,0.99745,6.4359 -3,0.80348,0.8661,0.86363,0.90028,0.82985,0.92007,0.27273,0.27273,0.27273,0.755,0.755,0.81818,0.81818,0.81818,0.755,0.755 -4,0.60521,0.79546,0.70608,0.853,0.60233,0.81482,0.33333,0.33333,0.33333,0.95,0.95,0.66667,0.66667,0.66667,0.95,0.95 -5,0.70601,0.67391,0.68496,0.65634,0.7162,0.83647,0.14286,0.13333,0.15385,2.1544,2.6123,0.64286,0.6,0.69231,2.1544,2.6123 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--- a/MIREX_data/2010/2009data/BV1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.5991,0.9096,0.6392,0.9328,0.4862,0.8130,0.2963,0.2667,0.3333,0.8148,0.7333,0.9167,0.9669,0.9886 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-0,struct_mrx_09_000273,0.5436,0.6524,0.4534,0.5161,0.4044,0.6965,0.2857,0.2857,0.2857,0.8571,0.8571,0.8571,1.2682,1.2682 -0,struct_mrx_09_000274,0.3844,0.5990,0.4139,0.5729,0.3240,0.6031,0.2609,0.2143,0.3333,0.5217,0.4286,0.6667,2.1386,3.8228 -0,struct_mrx_09_000275,0.6460,0.6875,0.4393,0.4256,0.4540,0.7256,0.2667,0.2222,0.3333,0.6000,0.5000,0.7500,1.3466,3.2318 -0,struct_mrx_09_000276,0.3850,0.4824,0.5544,0.6516,0.4824,0.6894,0.2353,0.2222,0.2500,0.7059,0.6667,0.7500,1.8524,2.3338 -0,struct_mrx_09_000277,0.6564,0.6136,0.5804,0.5390,0.6288,0.6841,0.3529,0.2308,0.7500,0.4706,0.3077,1.0000,0.1300,6.1351 -0,struct_mrx_09_000278,0.5661,0.6214,0.3882,0.4034,0.3741,0.6893,0.1936,0.1364,0.3333,0.5161,0.3636,0.8889,1.0099,3.1791 -0,struct_mrx_09_000279,0.5831,0.4674,0.4202,0.3772,0.4744,0.6048,0.2500,0.2000,0.3333,0.5833,0.4667,0.7778,1.3476,4.3539 -0,struct_mrx_09_000280,0.5266,0.6815,0.4654,0.5061,0.4308,0.6712,0.0870,0.0667,0.1250,0.5217,0.4000,0.7500,1.5408,4.6048 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--- a/MIREX_data/2010/2009data/BV2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.6856,0.9036,0.7453,0.9240,0.6246,0.8532,0.2963,0.2667,0.3333,0.8148,0.7333,0.9167,0.9669,0.9886 -0,struct_mrx_09_000001,0.7080,0.5550,0.4503,0.3522,0.6243,0.6951,0.2857,0.2105,0.4444,0.5714,0.4210,0.8889,0.9693,8.3100 -0,struct_mrx_09_000002,0.9158,0.1258,0.4738,0.3130,0.9742,0.3539,0.0645,0.0500,0.0909,0.3871,0.3000,0.5454,2.7396,5.8356 -0,struct_mrx_09_000003,0.8185,0.1087,0.3832,0.2440,0.8924,0.2914,0.1379,0.1111,0.1818,0.4828,0.3889,0.6364,2.6017,5.0493 -0,struct_mrx_09_000004,0.6306,0.4154,0.4416,0.3397,0.6307,0.5968,0.3871,0.3333,0.4615,0.6452,0.5556,0.7692,0.5365,1.9268 -0,struct_mrx_09_000005,0.3590,0.7666,0.3037,0.7287,0.1918,0.7040,0.1905,0.1176,0.5000,0.2857,0.1765,0.7500,1.0699,20.2920 -0,struct_mrx_09_000006,0.8141,0.4265,0.5744,0.4302,0.8640,0.5639,0.1667,0.1250,0.2500,0.6667,0.5000,1.0000,0.8510,4.4723 -0,struct_mrx_09_000007,0.7494,0.4680,0.4157,0.2991,0.6815,0.6870,0.2222,0.1667,0.3333,0.5000,0.3750,0.7500,1.4175,4.3807 -0,struct_mrx_09_000008,0.9028,0.4414,0.5664,0.4017,0.9603,0.5919,0.2000,0.1538,0.2857,0.6000,0.4615,0.8571,2.0987,2.1682 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--- a/MIREX_data/2010/2009data/GP7-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.5915,0.7893,0.6417,0.8248,0.5251,0.7970,0.1333,0.1111,0.1667,0.5333,0.4444,0.6667,1.9252,2.7713 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--- a/MIREX_data/2010/2009data/MHRAF2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.4827,0.5630,0.4728,0.5183,0.4347,0.6745,0.3636,0.4000,0.3333,0.6364,0.7000,0.5833,1.6012,0.8982 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-0,struct_mrx_09_000273,0.6661,0.7624,0.7112,0.7958,0.6429,0.8448,0.5185,0.5385,0.5000,0.8889,0.9231,0.8571,0.5272,0.4930 -0,struct_mrx_09_000274,0.6107,0.7536,0.6410,0.7962,0.5365,0.7549,0.1000,0.0909,0.1111,0.7000,0.6364,0.7778,1.5624,1.8984 -0,struct_mrx_09_000275,0.7345,0.5857,0.6013,0.4890,0.7806,0.7615,0.0952,0.1111,0.0833,0.4762,0.5556,0.4167,7.6137,1.4551 -0,struct_mrx_09_000276,0.4050,0.5352,0.4356,0.5366,0.3666,0.6231,0.4210,0.3636,0.5000,0.8421,0.7273,1.0000,0.4575,0.7817 -0,struct_mrx_09_000277,0.5004,0.7144,0.5604,0.7384,0.4516,0.7528,0.1667,0.1250,0.2500,0.3333,0.2500,0.5000,3.3859,6.3325 -0,struct_mrx_09_000278,0.5227,0.3066,0.3718,0.2712,0.5914,0.5253,0.1739,0.1429,0.2222,0.3478,0.2857,0.4444,3.2096,4.8601 -0,struct_mrx_09_000279,0.5707,0.5185,0.5760,0.5426,0.6138,0.7346,0.2353,0.2500,0.2222,0.4706,0.5000,0.4444,3.4222,2.9961 -0,struct_mrx_09_000280,0.9223,0.1744,0.5075,0.3468,0.9460,0.4195,0.0000,0.0000,0.0000,0.4000,0.4286,0.3750,8.9850,4.0965 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--- a/MIREX_data/2010/2009data/MND1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.6269,0.5903,0.5430,0.5021,0.5913,0.6650,0.2105,0.2857,0.1667,0.7368,1.0000,0.5833,1.0073,0.9869 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--- a/MIREX_data/2010/2009data/WB1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.8234,0.7480,0.8439,0.8046,0.8872,0.8919,0.2500,0.2500,0.2500,0.8333,0.8333,0.8333,0.5929,0.5155 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-0,struct_mrx_09_000041,0.6549,0.7834,0.6820,0.8044,0.5919,0.8635,0.0909,0.0909,0.0909,0.5454,0.5454,0.5454,2.1585,2.1585 -0,struct_mrx_09_000042,0.4216,0.4519,0.4722,0.5497,0.4138,0.5930,0.2500,0.1818,0.4000,0.3750,0.2727,0.6000,1.6409,7.5069 -0,struct_mrx_09_000043,0.8469,0.2948,0.4029,0.2599,0.8962,0.5575,0.0800,0.0909,0.0714,0.6400,0.7273,0.5714,2.3557,1.3884 -0,struct_mrx_09_000044,0.7367,0.5044,0.5073,0.3903,0.7245,0.6857,0.2963,0.2857,0.3077,0.3704,0.3571,0.3846,3.4193,2.7022 -0,struct_mrx_09_000045,0.4349,0.8090,0.5337,0.9059,0.3783,0.5688,0.0952,0.0769,0.1250,0.6667,0.5385,0.8750,1.4325,1.9875 -0,struct_mrx_09_000046,0.5474,0.7963,0.6198,0.8733,0.4803,0.7530,0.1053,0.0833,0.1429,0.5263,0.4167,0.7143,1.2835,2.4217 -0,struct_mrx_09_000047,0.6989,0.3952,0.3726,0.2604,0.6551,0.6841,0.1538,0.1538,0.1538,0.2308,0.2308,0.2308,6.6922,4.1240 -0,struct_mrx_09_000048,0.7030,0.4340,0.4795,0.3750,0.6649,0.6668,0.1250,0.1250,0.1250,0.2500,0.2500,0.2500,10.8020,4.0022 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-0,struct_mrx_09_000289,0.6144,0.4085,0.4846,0.4111,0.5902,0.7103,0.3333,0.2632,0.4546,0.6000,0.4737,0.8182,0.5650,1.2232 -0,struct_mrx_09_000290,0.7224,0.4202,0.3864,0.2761,0.6432,0.6781,0.1111,0.1429,0.0909,0.3333,0.4286,0.2727,18.0400,1.3450 -0,struct_mrx_09_000291,0.4937,0.5887,0.4522,0.5660,0.3765,0.6862,0.0000,0.0000,0.0000,0.4210,0.3077,0.6667,1.6621,5.0000 -0,struct_mrx_09_000292,0.6308,0.5678,0.5652,0.5430,0.5893,0.6937,0.1905,0.2000,0.1818,0.2857,0.3000,0.2727,6.9633,3.5191 -0,struct_mrx_09_000293,0.5478,0.6381,0.5001,0.6154,0.4212,0.7353,0.1053,0.1111,0.1000,0.5263,0.5556,0.5000,3.7022,2.7476 -0,struct_mrx_09_000294,0.7170,0.5424,0.6185,0.5257,0.7510,0.8159,0.0833,0.0769,0.0909,0.5000,0.4615,0.5454,2.5432,1.3100 -0,struct_mrx_09_000295,0.6836,0.8145,0.6189,0.7562,0.5238,0.8568,0.4000,0.3333,0.5000,0.7000,0.5833,0.8750,0.5056,0.5496 -0,struct_mrx_09_000296,0.7583,0.5498,0.6775,0.5722,0.8304,0.7619,0.4348,0.5000,0.3846,0.6956,0.8000,0.6154,0.9203,0.3366
--- a/MIREX_data/2010/2010data/BV1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8855,0.7380,0.5976,0.4656,0.8339,0.9051,0.2759,0.2667,0.2857,0.7586,0.7333,0.7857,1.6644,2.2586 -0,struct_mrx_10_000001,0.8864,0.4749,0.2014,0.1156,0.7818,0.6937,0.1778,0.1818,0.1739,0.5333,0.5454,0.5217,2.4905,2.4915 -0,struct_mrx_10_000002,0.8618,0.5596,0.3446,0.2208,0.7845,0.8271,0.2500,0.2500,0.2500,0.4000,0.4000,0.4000,3.3744,3.7544 -0,struct_mrx_10_000003,0.9342,0.7258,0.5844,0.4303,0.9106,0.9205,0.2703,0.2941,0.2500,0.7568,0.8235,0.7000,1.0118,0.9627 -0,struct_mrx_10_000004,0.8478,0.5302,0.3127,0.1964,0.7666,0.8245,0.0930,0.0952,0.0909,0.5116,0.5238,0.5000,2.4504,2.5889 -0,struct_mrx_10_000005,0.8380,0.7433,0.5555,0.4584,0.7047,0.9336,0.2791,0.2609,0.3000,0.6512,0.6087,0.7000,1.1846,2.4631 -0,struct_mrx_10_000006,0.9084,0.6558,0.5007,0.3475,0.8952,0.8691,0.3125,0.3571,0.2778,0.8125,0.9286,0.7222,1.1694,1.0387 -0,struct_mrx_10_000007,0.8590,0.5720,0.3151,0.1976,0.7777,0.8028,0.0930,0.0870,0.1000,0.6512,0.6087,0.7000,1.9212,2.6253 -0,struct_mrx_10_000008,0.9049,0.7023,0.5394,0.3976,0.8383,0.9140,0.3590,0.3889,0.3333,0.7692,0.8333,0.7143,0.7421,0.7062 -0,struct_mrx_10_000009,0.8543,0.6330,0.3853,0.2606,0.7389,0.8756,0.1905,0.2000,0.1818,0.5238,0.5500,0.5000,2.7939,2.7939 -0,struct_mrx_10_000010,0.8765,0.5803,0.3811,0.2474,0.8299,0.8310,0.2941,0.3125,0.2778,0.7647,0.8125,0.7222,1.9076,1.9076 -0,struct_mrx_10_000011,0.8384,0.7626,0.6167,0.5208,0.7559,0.9229,0.2581,0.2500,0.2667,0.5806,0.5625,0.6000,1.7096,1.7216 -0,struct_mrx_10_000012,0.9390,0.6652,0.4290,0.2830,0.8855,0.8676,0.4103,0.4210,0.4000,0.8205,0.8421,0.8000,0.9024,0.9315 -0,struct_mrx_10_000013,0.8449,0.6895,0.4044,0.2803,0.7256,0.8709,0.0426,0.0417,0.0435,0.5957,0.5833,0.6087,2.0141,2.6113 -0,struct_mrx_10_000014,0.9033,0.6402,0.4053,0.2649,0.8621,0.8464,0.2632,0.2941,0.2381,0.6842,0.7647,0.6190,1.3415,1.3233 -0,struct_mrx_10_000015,0.9103,0.7160,0.5450,0.3951,0.8782,0.9053,0.1667,0.1765,0.1579,0.7778,0.8235,0.7368,0.9359,0.9362 -0,struct_mrx_10_000016,0.8987,0.5914,0.4193,0.2745,0.8876,0.8454,0.2857,0.3125,0.2632,0.8571,0.9375,0.7895,0.9312,0.9197 -0,struct_mrx_10_000017,0.8156,0.6916,0.5240,0.4174,0.7037,0.9114,0.2162,0.2000,0.2353,0.6486,0.6000,0.7059,0.6081,0.9919 -0,struct_mrx_10_000018,0.8714,0.7154,0.5244,0.3943,0.7825,0.9213,0.4390,0.4500,0.4286,0.6829,0.7000,0.6667,0.7888,1.1579 -0,struct_mrx_10_000019,0.8689,0.4580,0.2393,0.1404,0.8096,0.7696,0.0952,0.1111,0.0833,0.4762,0.5556,0.4167,3.4848,2.9004 -0,struct_mrx_10_000020,0.8183,0.6970,0.4719,0.3634,0.6726,0.8950,0.2381,0.2273,0.2500,0.5714,0.5454,0.6000,2.3928,2.7039 -0,struct_mrx_10_000021,0.8363,0.7882,0.6000,0.5004,0.7493,0.9165,0.1081,0.0952,0.1250,0.4865,0.4286,0.5625,0.9550,3.3392 -0,struct_mrx_10_000022,0.8605,0.7256,0.5558,0.4255,0.8010,0.8773,0.2632,0.2083,0.3571,0.6316,0.5000,0.8571,0.8161,3.7346 -0,struct_mrx_10_000023,0.8418,0.6944,0.5333,0.4139,0.7494,0.8758,0.3226,0.3333,0.3125,0.5161,0.5333,0.5000,1.3823,1.9480 -0,struct_mrx_10_000024,0.8289,0.7716,0.5820,0.4778,0.7442,0.8991,0.3871,0.3529,0.4286,0.5806,0.5294,0.6429,0.5363,2.1956 -0,struct_mrx_10_000025,0.9192,0.6688,0.4459,0.2979,0.8859,0.8875,0.0952,0.1053,0.0870,0.7619,0.8421,0.6956,1.4113,1.3216 -0,struct_mrx_10_000026,0.8809,0.6008,0.3437,0.2183,0.8073,0.8333,0.3404,0.3200,0.3636,0.7234,0.6800,0.7727,0.9302,2.2189 -0,struct_mrx_10_000027,0.8744,0.6572,0.3918,0.2617,0.7790,0.8772,0.4444,0.4348,0.4546,0.6667,0.6522,0.6818,0.6949,0.9626 -0,struct_mrx_10_000028,0.8586,0.6545,0.3832,0.2587,0.7390,0.8746,0.2273,0.2381,0.2174,0.5909,0.6190,0.5652,1.1886,1.6507 -0,struct_mrx_10_000029,0.8686,0.6654,0.4958,0.3578,0.8072,0.8678,0.1379,0.1538,0.1250,0.5517,0.6154,0.5000,2.5363,2.2227 -0,struct_mrx_10_000030,0.9171,0.6252,0.4786,0.3273,0.8895,0.8800,0.3871,0.5000,0.3158,0.5161,0.6667,0.4210,1.9256,0.8668 -0,struct_mrx_10_000031,0.8692,0.7577,0.5587,0.4375,0.7726,0.9182,0.3684,0.3500,0.3889,0.6316,0.6000,0.6667,0.9374,1.3360 -0,struct_mrx_10_000032,0.8590,0.7151,0.5128,0.3855,0.7657,0.9133,0.3256,0.3333,0.3182,0.7442,0.7619,0.7273,0.9447,0.9500 -0,struct_mrx_10_000033,0.8439,0.6888,0.5370,0.4069,0.7895,0.8751,0.2759,0.2667,0.2857,0.6897,0.6667,0.7143,1.1996,1.7659 -0,struct_mrx_10_000034,0.8710,0.6982,0.4565,0.3238,0.7739,0.8934,0.1951,0.1905,0.2000,0.7805,0.7619,0.8000,1.8047,1.9947 -0,struct_mrx_10_000035,0.8768,0.6722,0.5070,0.3678,0.8160,0.8793,0.4118,0.4375,0.3889,0.7059,0.7500,0.6667,1.0130,1.0130 -0,struct_mrx_10_000036,0.8372,0.7529,0.5413,0.4342,0.7185,0.8921,0.2778,0.2381,0.3333,0.7222,0.6190,0.8667,0.7010,1.1773 -0,struct_mrx_10_000037,0.8714,0.5463,0.3546,0.2255,0.8294,0.7833,0.3243,0.3158,0.3333,0.5946,0.5790,0.6111,2.3576,2.9486 -0,struct_mrx_10_000038,0.8528,0.7476,0.5714,0.4577,0.7602,0.9146,0.2353,0.2222,0.2500,0.7059,0.6667,0.7500,1.2730,1.9810 -0,struct_mrx_10_000039,0.8655,0.5611,0.3281,0.2073,0.7859,0.7847,0.2051,0.2000,0.2105,0.6154,0.6000,0.6316,2.4433,2.4642 -0,struct_mrx_10_000040,0.8460,0.7347,0.5127,0.3947,0.7312,0.9139,0.1026,0.1053,0.1000,0.6154,0.6316,0.6000,1.0481,1.0504 -0,struct_mrx_10_000041,0.7860,0.6960,0.4529,0.3604,0.6093,0.9160,0.2128,0.1923,0.2381,0.6808,0.6154,0.7619,0.9458,2.0053 -0,struct_mrx_10_000042,0.8017,0.6746,0.4461,0.3331,0.6751,0.8496,0.3429,0.3333,0.3529,0.5714,0.5556,0.5882,1.4663,2.7611 -0,struct_mrx_10_000043,0.8695,0.4329,0.2221,0.1284,0.8192,0.6254,0.2222,0.2353,0.2105,0.6667,0.7059,0.6316,1.6827,1.6827 -0,struct_mrx_10_000044,0.8319,0.7688,0.5384,0.4518,0.6662,0.9163,0.5500,0.5000,0.6111,0.7500,0.6818,0.8333,0.3425,0.6035 -0,struct_mrx_10_000045,0.8886,0.5710,0.3414,0.2157,0.8187,0.8566,0.2083,0.2174,0.2000,0.4583,0.4783,0.4400,3.0865,3.0865 -0,struct_mrx_10_000046,0.8696,0.6928,0.5082,0.3756,0.7854,0.9068,0.4324,0.4706,0.4000,0.7568,0.8235,0.7000,0.6985,0.5190 -0,struct_mrx_10_000047,0.8604,0.6164,0.3865,0.2575,0.7744,0.8526,0.1026,0.1053,0.1000,0.6154,0.6316,0.6000,1.0519,1.2596 -0,struct_mrx_10_000048,0.8706,0.5676,0.3834,0.2497,0.8259,0.8183,0.3125,0.3846,0.2632,0.5625,0.6923,0.4737,1.0499,1.0471 -0,struct_mrx_10_000049,0.7655,0.6435,0.4616,0.3563,0.6551,0.8672,0.2500,0.2353,0.2667,0.5000,0.4706,0.5333,1.4278,3.6661 -0,struct_mrx_10_000050,0.8901,0.5372,0.2814,0.1693,0.8322,0.7866,0.1860,0.2000,0.1739,0.6046,0.6500,0.5652,1.5201,1.4725 -0,struct_mrx_10_000051,0.7247,0.6531,0.4266,0.3436,0.5626,0.8401,0.0625,0.0526,0.0769,0.4375,0.3684,0.5385,1.4634,5.9190 -0,struct_mrx_10_000052,0.8703,0.6071,0.4142,0.2762,0.8276,0.8241,0.2667,0.3077,0.2353,0.6667,0.7692,0.5882,1.9139,1.4659 -0,struct_mrx_10_000053,0.9467,0.5694,0.3505,0.2163,0.9226,0.8465,0.3500,0.4667,0.2800,0.6000,0.8000,0.4800,2.1322,0.5094 -0,struct_mrx_10_000054,0.8034,0.7107,0.5227,0.4168,0.7006,0.8888,0.1053,0.0870,0.1333,0.4210,0.3478,0.5333,1.5371,4.7453 -0,struct_mrx_10_000055,0.8518,0.7203,0.5545,0.4387,0.7534,0.8924,0.1875,0.1667,0.2143,0.5625,0.5000,0.6429,1.3891,2.5461 -0,struct_mrx_10_000056,0.8113,0.5886,0.4280,0.2983,0.7572,0.8444,0.1212,0.1250,0.1176,0.6667,0.6875,0.6471,1.7789,1.7800 -0,struct_mrx_10_000057,0.8768,0.6792,0.4424,0.3100,0.7721,0.8904,0.3256,0.3500,0.3044,0.5581,0.6000,0.5217,1.7038,1.3823 -0,struct_mrx_10_000058,0.8969,0.7328,0.5068,0.3654,0.8264,0.9077,0.3721,0.4000,0.3478,0.7442,0.8000,0.6956,0.9319,0.9270 -0,struct_mrx_10_000059,0.8747,0.7234,0.5663,0.4416,0.7892,0.9204,0.2927,0.2857,0.3000,0.6829,0.6667,0.7000,0.7637,1.0977 -0,struct_mrx_10_000060,0.8504,0.5907,0.3669,0.2409,0.7695,0.8214,0.1053,0.1176,0.0952,0.6842,0.7647,0.6190,1.1785,1.3835 -0,struct_mrx_10_000061,0.6946,0.6170,0.4602,0.3859,0.5700,0.8752,0.0690,0.0667,0.0714,0.6897,0.6667,0.7143,1.8014,2.3263 -0,struct_mrx_10_000062,0.8293,0.7226,0.5311,0.4168,0.7317,0.8841,0.2000,0.2000,0.2000,0.6667,0.6667,0.6667,1.0225,1.0240 -0,struct_mrx_10_000063,1.0000,0.0311,0.0949,0.0498,1.0000,0.0507,0.1600,1.0000,0.0870,0.1600,1.0000,0.0870,58.2640,0.0183 -0,struct_mrx_10_000064,0.8131,0.6275,0.4564,0.3329,0.7256,0.8133,0.2667,0.2353,0.3077,0.6667,0.5882,0.7692,0.7879,1.8715 -0,struct_mrx_10_000065,0.7478,0.6241,0.3573,0.2556,0.5935,0.7959,0.1212,0.1053,0.1429,0.4848,0.4210,0.5714,2.0225,5.1070 -0,struct_mrx_10_000066,0.8059,0.5364,0.3120,0.2036,0.6675,0.8089,0.0488,0.0435,0.0556,0.4390,0.3913,0.5000,2.5316,3.9098 -0,struct_mrx_10_000067,0.8739,0.6700,0.5103,0.3733,0.8059,0.9058,0.2162,0.2222,0.2105,0.7568,0.7778,0.7368,1.5860,1.5876 -0,struct_mrx_10_000068,0.7864,0.5177,0.2929,0.1879,0.6640,0.7218,0.0571,0.0476,0.0714,0.4571,0.3810,0.5714,2.0788,4.4628 -0,struct_mrx_10_000069,0.8360,0.6560,0.4346,0.3098,0.7273,0.8514,0.2941,0.2778,0.3125,0.5882,0.5556,0.6250,1.6033,2.1218 -0,struct_mrx_10_000070,0.7292,0.5504,0.3014,0.2050,0.5694,0.7518,0.2564,0.2083,0.3333,0.5641,0.4583,0.7333,0.6352,5.3765 -0,struct_mrx_10_000071,0.9134,0.7941,0.6603,0.5380,0.8546,0.9507,0.4286,0.4091,0.4500,0.8095,0.7727,0.8500,0.6958,0.9583 -0,struct_mrx_10_000072,0.7302,0.6830,0.5352,0.4625,0.6350,0.8755,0.0800,0.0833,0.0769,0.4000,0.4167,0.3846,2.8168,3.8930 -0,struct_mrx_10_000073,0.8790,0.6843,0.4652,0.3293,0.7922,0.8952,0.2000,0.2105,0.1905,0.6000,0.6316,0.5714,1.0154,1.4422 -0,struct_mrx_10_000074,0.7630,0.6459,0.4391,0.3336,0.6422,0.8281,0.0741,0.0667,0.0833,0.5926,0.5333,0.6667,1.6169,2.3907 -0,struct_mrx_10_000075,0.8314,0.6567,0.4031,0.2816,0.7090,0.8766,0.0465,0.0455,0.0476,0.4651,0.4546,0.4762,2.3304,3.4116 -0,struct_mrx_10_000076,0.8208,0.6313,0.4367,0.3153,0.7101,0.8554,0.3030,0.2941,0.3125,0.5454,0.5294,0.5625,1.3742,2.7142 -0,struct_mrx_10_000077,0.8398,0.7575,0.5343,0.4250,0.7193,0.9058,0.2564,0.2273,0.2941,0.5128,0.4546,0.5882,1.2755,3.4033 -0,struct_mrx_10_000078,0.7323,0.7286,0.4429,0.3970,0.5009,0.8923,0.1333,0.1034,0.1875,0.4444,0.3448,0.6250,1.0191,6.8232 -0,struct_mrx_10_000079,0.8370,0.4666,0.2351,0.1398,0.7374,0.6601,0.1667,0.1579,0.1765,0.3889,0.3684,0.4118,3.2911,3.8789 -0,struct_mrx_10_000080,0.8047,0.6771,0.4026,0.2971,0.6244,0.9039,0.0408,0.0385,0.0435,0.2449,0.2308,0.2609,4.2803,4.4481 -0,struct_mrx_10_000081,0.8342,0.6119,0.3960,0.2713,0.7325,0.8739,0.2500,0.2632,0.2381,0.5500,0.5790,0.5238,1.6879,1.6896 -0,struct_mrx_10_000082,0.7863,0.6714,0.4745,0.3745,0.6476,0.8893,0.1143,0.1250,0.1053,0.5714,0.6250,0.5263,1.8800,2.0994 -0,struct_mrx_10_000083,0.7778,0.7394,0.5568,0.4915,0.6423,0.8839,0.1765,0.1429,0.2308,0.5294,0.4286,0.6923,1.1910,4.5702 -0,struct_mrx_10_000084,0.8493,0.7738,0.5864,0.4847,0.7422,0.9316,0.2564,0.2500,0.2632,0.6154,0.6000,0.6316,1.2813,1.5461 -0,struct_mrx_10_000085,0.7960,0.6002,0.3189,0.2120,0.6433,0.7795,0.2632,0.2381,0.2941,0.5263,0.4762,0.5882,1.2170,3.3376 -0,struct_mrx_10_000086,0.7990,0.7533,0.5672,0.4939,0.6662,0.9228,0.1667,0.1579,0.1765,0.3889,0.3684,0.4118,2.8416,4.8436 -0,struct_mrx_10_000087,0.9086,0.6829,0.4863,0.3429,0.8358,0.9172,0.3256,0.4118,0.2692,0.6512,0.8235,0.5385,2.1972,0.9240 -0,struct_mrx_10_000088,0.8614,0.6540,0.4084,0.2779,0.7697,0.8585,0.2051,0.2000,0.2105,0.6154,0.6000,0.6316,2.3473,2.3986 -0,struct_mrx_10_000089,0.8391,0.6552,0.4428,0.3166,0.7361,0.8935,0.2000,0.2105,0.1905,0.7500,0.7895,0.7143,1.7780,1.7780 -0,struct_mrx_10_000090,0.8296,0.7574,0.5665,0.4740,0.7037,0.9250,0.2632,0.2381,0.2941,0.7368,0.6667,0.8235,1.0162,1.4198 -0,struct_mrx_10_000091,0.7302,0.7028,0.4666,0.3973,0.5651,0.8500,0.3226,0.2632,0.4167,0.5161,0.4210,0.6667,0.7994,3.6347 -0,struct_mrx_10_000092,0.8385,0.6077,0.3375,0.2223,0.7000,0.8716,0.0851,0.0870,0.0833,0.6383,0.6522,0.6250,2.5283,2.5839 -0,struct_mrx_10_000093,0.8850,0.7133,0.4893,0.3514,0.8054,0.9107,0.2326,0.2381,0.2273,0.7907,0.8095,0.7727,0.7530,0.7607 -0,struct_mrx_10_000094,0.8461,0.7104,0.4166,0.3001,0.6808,0.8818,0.2553,0.2500,0.2609,0.6383,0.6250,0.6522,0.8565,1.2154 -0,struct_mrx_10_000095,0.8456,0.7914,0.6359,0.5494,0.7547,0.9304,0.1667,0.1579,0.1765,0.6667,0.6316,0.7059,1.3514,2.1586 -0,struct_mrx_10_000096,0.8678,0.6723,0.4865,0.3546,0.7746,0.9234,0.3404,0.3478,0.3333,0.8511,0.8696,0.8333,1.1624,1.2568 -0,struct_mrx_10_000097,0.8527,0.5886,0.3618,0.2368,0.7662,0.8489,0.1579,0.1875,0.1364,0.4210,0.5000,0.3636,2.6215,4.0308 -0,struct_mrx_10_000098,0.8859,0.7333,0.4689,0.3337,0.7885,0.9073,0.4444,0.4348,0.4546,0.7111,0.6956,0.7273,0.4826,0.7451 -0,struct_mrx_10_000099,0.8402,0.7378,0.5268,0.4184,0.7110,0.9255,0.2791,0.2727,0.2857,0.5116,0.5000,0.5238,1.6282,2.8953
--- a/MIREX_data/2010/2010data/BV2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.9236,0.5254,0.3530,0.2190,0.9094,0.7175,0.2759,0.2667,0.2857,0.7586,0.7333,0.7857,1.6644,2.2586 -0,struct_mrx_10_000001,0.9466,0.2721,0.1481,0.0805,0.9240,0.4717,0.1778,0.1818,0.1739,0.5333,0.5454,0.5217,2.4905,2.4915 -0,struct_mrx_10_000002,0.9074,0.2260,0.1557,0.0850,0.9302,0.4162,0.2500,0.2500,0.2500,0.4000,0.4000,0.4000,3.3744,3.7544 -0,struct_mrx_10_000003,0.9418,0.5313,0.3787,0.2370,0.9424,0.8085,0.2703,0.2941,0.2500,0.7568,0.8235,0.7000,1.0118,0.9627 -0,struct_mrx_10_000004,0.9416,0.0642,0.1035,0.0547,0.9647,0.1237,0.0930,0.0952,0.0909,0.5116,0.5238,0.5000,2.4504,2.5889 -0,struct_mrx_10_000005,0.8719,0.4351,0.2173,0.1254,0.8118,0.6555,0.2791,0.2609,0.3000,0.6512,0.6087,0.7000,1.1846,2.4631 -0,struct_mrx_10_000006,0.8938,0.5167,0.3981,0.2549,0.9089,0.7999,0.3125,0.3571,0.2778,0.8125,0.9286,0.7222,1.1694,1.0387 -0,struct_mrx_10_000007,0.8259,0.1892,0.1444,0.0789,0.8536,0.4127,0.0930,0.0870,0.1000,0.6512,0.6087,0.7000,1.9212,2.6253 -0,struct_mrx_10_000008,0.9177,0.3820,0.1997,0.1127,0.8778,0.5805,0.3590,0.3889,0.3333,0.7692,0.8333,0.7143,0.7421,0.7062 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-0,struct_mrx_10_000025,0.9354,0.4668,0.2364,0.1356,0.9211,0.6961,0.0952,0.1053,0.0870,0.7619,0.8421,0.6956,1.4113,1.3216 -0,struct_mrx_10_000026,0.9240,0.3743,0.2233,0.1267,0.9389,0.6453,0.3404,0.3200,0.3636,0.7234,0.6800,0.7727,0.9302,2.2189 -0,struct_mrx_10_000027,0.9286,0.4086,0.1894,0.1057,0.9055,0.6115,0.4444,0.4348,0.4546,0.6667,0.6522,0.6818,0.6949,0.9626 -0,struct_mrx_10_000028,0.9063,0.4557,0.2076,0.1179,0.8683,0.6486,0.2273,0.2381,0.2174,0.5909,0.6190,0.5652,1.1886,1.6507 -0,struct_mrx_10_000029,0.8886,0.5465,0.4035,0.2624,0.8726,0.7924,0.1379,0.1538,0.1250,0.5517,0.6154,0.5000,2.5363,2.2227 -0,struct_mrx_10_000030,0.9528,0.4550,0.2768,0.1619,0.9538,0.6917,0.3871,0.5000,0.3158,0.5161,0.6667,0.4210,1.9256,0.8668 -0,struct_mrx_10_000031,0.8843,0.6083,0.4088,0.2709,0.8329,0.8381,0.3684,0.3500,0.3889,0.6316,0.6000,0.6667,0.9374,1.3360 -0,struct_mrx_10_000032,0.9326,0.4434,0.2443,0.1406,0.9298,0.6512,0.3256,0.3333,0.3182,0.7442,0.7619,0.7273,0.9447,0.9500 -0,struct_mrx_10_000033,0.8436,0.4646,0.3619,0.2307,0.8388,0.7281,0.2759,0.2667,0.2857,0.6897,0.6667,0.7143,1.1996,1.7659 -0,struct_mrx_10_000034,0.9281,0.4584,0.2239,0.1279,0.8964,0.6406,0.1951,0.1905,0.2000,0.7805,0.7619,0.8000,1.8047,1.9947 -0,struct_mrx_10_000035,0.9219,0.4799,0.3409,0.2091,0.9212,0.7293,0.4118,0.4375,0.3889,0.7059,0.7500,0.6667,1.0130,1.0130 -0,struct_mrx_10_000036,0.8483,0.3970,0.2594,0.1548,0.8008,0.5925,0.2778,0.2381,0.3333,0.7222,0.6190,0.8667,0.7010,1.1773 -0,struct_mrx_10_000037,0.9884,0.1328,0.1479,0.0799,0.9936,0.1832,0.3243,0.3158,0.3333,0.5946,0.5790,0.6111,2.3576,2.9486 -0,struct_mrx_10_000038,0.8954,0.6077,0.4167,0.2754,0.8553,0.8208,0.2353,0.2222,0.2500,0.7059,0.6667,0.7500,1.2730,1.9810 -0,struct_mrx_10_000039,0.9240,0.1660,0.1450,0.0787,0.9178,0.2735,0.2051,0.2000,0.2105,0.6154,0.6000,0.6316,2.4433,2.4642 -0,struct_mrx_10_000040,0.8457,0.6560,0.4614,0.3315,0.7585,0.8903,0.1026,0.1053,0.1000,0.6154,0.6316,0.6000,1.0481,1.0504 -0,struct_mrx_10_000041,0.8093,0.5048,0.2726,0.1691,0.7028,0.7868,0.2128,0.1923,0.2381,0.6808,0.6154,0.7619,0.9458,2.0053 -0,struct_mrx_10_000042,0.8080,0.5149,0.3710,0.2452,0.7620,0.7703,0.3429,0.3333,0.3529,0.5714,0.5556,0.5882,1.4663,2.7611 -0,struct_mrx_10_000043,0.9482,0.2521,0.1752,0.0964,0.9662,0.4048,0.2222,0.2353,0.2105,0.6667,0.7059,0.6316,1.6827,1.6827 -0,struct_mrx_10_000044,0.8750,0.5799,0.3381,0.2147,0.7947,0.7700,0.5500,0.5000,0.6111,0.7500,0.6818,0.8333,0.3425,0.6035 -0,struct_mrx_10_000045,0.8558,0.2119,0.1463,0.0794,0.9269,0.5054,0.2083,0.2174,0.2000,0.4583,0.4783,0.4400,3.0865,3.0865 -0,struct_mrx_10_000046,0.9060,0.5794,0.4056,0.2628,0.8880,0.8385,0.4324,0.4706,0.4000,0.7568,0.8235,0.7000,0.6985,0.5190 -0,struct_mrx_10_000047,0.8430,0.3712,0.2158,0.1245,0.8093,0.6445,0.1026,0.1053,0.1000,0.6154,0.6316,0.6000,1.0519,1.2596 -0,struct_mrx_10_000048,0.9279,0.3549,0.2170,0.1228,0.9298,0.5452,0.3125,0.3846,0.2632,0.5625,0.6923,0.4737,1.0499,1.0471 -0,struct_mrx_10_000049,0.8557,0.4027,0.2440,0.1432,0.8239,0.5597,0.2500,0.2353,0.2667,0.5000,0.4706,0.5333,1.4278,3.6661 -0,struct_mrx_10_000050,0.8978,0.2268,0.1477,0.0804,0.9078,0.4736,0.1860,0.2000,0.1739,0.6046,0.6500,0.5652,1.5201,1.4725 -0,struct_mrx_10_000051,0.8269,0.3678,0.2547,0.1522,0.7811,0.5170,0.0625,0.0526,0.0769,0.4375,0.3684,0.5385,1.4634,5.9190 -0,struct_mrx_10_000052,0.9205,0.3294,0.2163,0.1224,0.9300,0.4886,0.2667,0.3077,0.2353,0.6667,0.7692,0.5882,1.9139,1.4659 -0,struct_mrx_10_000053,0.9311,0.4052,0.2396,0.1376,0.9226,0.7367,0.3500,0.4667,0.2800,0.6000,0.8000,0.4800,2.1322,0.5094 -0,struct_mrx_10_000054,0.8462,0.3564,0.2516,0.1480,0.8370,0.5606,0.1053,0.0870,0.1333,0.4210,0.3478,0.5333,1.5371,4.7453 -0,struct_mrx_10_000055,0.9214,0.4032,0.2934,0.1751,0.9027,0.6123,0.1875,0.1667,0.2143,0.5625,0.5000,0.6429,1.3891,2.5461 -0,struct_mrx_10_000056,0.7692,0.8586,0.5626,0.6180,0.5164,0.9387,0.2564,0.2273,0.2941,0.6667,0.5909,0.7647,0.8888,2.2643 -0,struct_mrx_10_000057,0.9282,0.5595,0.3247,0.1981,0.8994,0.7881,0.3256,0.3500,0.3044,0.5581,0.6000,0.5217,1.7038,1.3823 -0,struct_mrx_10_000058,0.8937,0.5879,0.3826,0.2474,0.8443,0.8450,0.3721,0.4000,0.3478,0.7442,0.8000,0.6956,0.9319,0.9270 -0,struct_mrx_10_000059,0.9265,0.2590,0.1649,0.0907,0.9094,0.3834,0.2927,0.2857,0.3000,0.6829,0.6667,0.7000,0.7637,1.0977 -0,struct_mrx_10_000060,0.8188,0.2263,0.1728,0.0955,0.9074,0.4183,0.1053,0.1176,0.0952,0.6842,0.7647,0.6190,1.1785,1.3835 -0,struct_mrx_10_000061,0.8657,0.3688,0.2756,0.1632,0.8848,0.5665,0.0690,0.0667,0.0714,0.6897,0.6667,0.7143,1.8014,2.3263 -0,struct_mrx_10_000062,0.8164,0.5849,0.4156,0.2876,0.7488,0.8095,0.2000,0.2000,0.2000,0.6667,0.6667,0.6667,1.0225,1.0240 -0,struct_mrx_10_000063,0.9838,0.0706,0.1012,0.0534,0.9798,0.1321,0.2222,0.2273,0.2174,0.4444,0.4546,0.4348,3.1111,3.3043 -0,struct_mrx_10_000064,0.9675,0.2200,0.2375,0.1349,0.9908,0.3089,0.2667,0.2353,0.3077,0.6667,0.5882,0.7692,0.7879,1.8715 -0,struct_mrx_10_000065,0.7692,0.4825,0.2848,0.1811,0.6658,0.6828,0.1212,0.1053,0.1429,0.4848,0.4210,0.5714,2.0225,5.1070 -0,struct_mrx_10_000066,0.7743,0.3276,0.2377,0.1408,0.7609,0.6828,0.0488,0.0435,0.0556,0.4390,0.3913,0.5000,2.5316,3.9098 -0,struct_mrx_10_000067,0.8454,0.3398,0.2464,0.1436,0.8677,0.6752,0.2162,0.2222,0.2105,0.7568,0.7778,0.7368,1.5860,1.5876 -0,struct_mrx_10_000068,0.9046,0.1014,0.1702,0.0937,0.9277,0.2054,0.0556,0.0455,0.0714,0.4444,0.3636,0.5714,1.7591,4.3206 -0,struct_mrx_10_000069,0.9071,0.3802,0.2332,0.1341,0.8935,0.5411,0.2941,0.2778,0.3125,0.5882,0.5556,0.6250,1.6033,2.1218 -0,struct_mrx_10_000070,0.9287,0.1988,0.1974,0.1103,0.9417,0.2766,0.2564,0.2083,0.3333,0.5641,0.4583,0.7333,0.6352,5.3765 -0,struct_mrx_10_000071,0.9264,0.4869,0.2735,0.1612,0.9022,0.7318,0.4286,0.4091,0.4500,0.8095,0.7727,0.8500,0.6958,0.9583 -0,struct_mrx_10_000072,0.7577,0.6338,0.5282,0.4208,0.7090,0.8577,0.0800,0.0833,0.0769,0.4000,0.4167,0.3846,2.8168,3.8930 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-0,struct_mrx_10_000081,0.8628,0.5219,0.3542,0.2251,0.8299,0.8291,0.2500,0.2632,0.2381,0.5500,0.5790,0.5238,1.6879,1.6896 -0,struct_mrx_10_000082,0.8275,0.5368,0.3461,0.2262,0.7368,0.7881,0.1143,0.1250,0.1053,0.5714,0.6250,0.5263,1.8800,2.0994 -0,struct_mrx_10_000083,0.8364,0.4061,0.3490,0.2218,0.8177,0.6556,0.1765,0.1429,0.2308,0.5294,0.4286,0.6923,1.1910,4.5702 -0,struct_mrx_10_000084,0.8240,0.6172,0.4292,0.3019,0.7422,0.8716,0.2564,0.2500,0.2632,0.6154,0.6000,0.6316,1.2813,1.5461 -0,struct_mrx_10_000085,0.8898,0.3280,0.2033,0.1150,0.8764,0.4546,0.2632,0.2381,0.2941,0.5263,0.4762,0.5882,1.2170,3.3376 -0,struct_mrx_10_000086,0.8797,0.6153,0.4178,0.2787,0.8342,0.8234,0.1667,0.1579,0.1765,0.3889,0.3684,0.4118,2.8416,4.8436 -0,struct_mrx_10_000087,0.9176,0.5591,0.3459,0.2156,0.8740,0.8448,0.3256,0.4118,0.2692,0.6512,0.8235,0.5385,2.1972,0.9240 -0,struct_mrx_10_000088,0.9264,0.5469,0.3550,0.2200,0.9192,0.7886,0.2051,0.2000,0.2105,0.6154,0.6000,0.6316,2.3473,2.3986 -0,struct_mrx_10_000089,0.8824,0.4851,0.2947,0.1783,0.8492,0.7674,0.2000,0.2105,0.1905,0.7500,0.7895,0.7143,1.7780,1.7780 -0,struct_mrx_10_000090,0.8527,0.6743,0.4896,0.3585,0.7718,0.8880,0.2632,0.2381,0.2941,0.7368,0.6667,0.8235,1.0162,1.4198 -0,struct_mrx_10_000091,0.7557,0.5091,0.3488,0.2357,0.6711,0.7096,0.3226,0.2632,0.4167,0.5161,0.4210,0.6667,0.7994,3.6347 -0,struct_mrx_10_000092,0.8820,0.3332,0.1508,0.0831,0.8170,0.5682,0.0851,0.0870,0.0833,0.6383,0.6522,0.6250,2.5283,2.5839 -0,struct_mrx_10_000093,0.8870,0.5669,0.3723,0.2386,0.8461,0.8495,0.2326,0.2381,0.2273,0.7907,0.8095,0.7727,0.7530,0.7607 -0,struct_mrx_10_000094,0.9042,0.6083,0.3990,0.2589,0.8696,0.8365,0.2553,0.2500,0.2609,0.6383,0.6250,0.6522,0.8565,1.2154 -0,struct_mrx_10_000095,0.8746,0.6170,0.4305,0.2890,0.8430,0.8192,0.1667,0.1579,0.1765,0.6667,0.6316,0.7059,1.3514,2.1586 -0,struct_mrx_10_000096,0.9236,0.2892,0.1396,0.0757,0.8895,0.4830,0.3404,0.3478,0.3333,0.8511,0.8696,0.8333,1.1624,1.2568 -0,struct_mrx_10_000097,0.8781,0.3871,0.2271,0.1307,0.8659,0.6697,0.1579,0.1875,0.1364,0.4210,0.5000,0.3636,2.6215,4.0308 -0,struct_mrx_10_000098,0.9091,0.5340,0.2592,0.1527,0.8578,0.7462,0.4444,0.4348,0.4546,0.7111,0.6956,0.7273,0.4826,0.7451 -0,struct_mrx_10_000099,0.8553,0.5511,0.3406,0.2178,0.7814,0.8239,0.2791,0.2727,0.2857,0.5116,0.5000,0.5238,1.6282,2.8953
--- a/MIREX_data/2010/2010data/GP7-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8990,0.6372,0.4996,0.3483,0.8838,0.8508,0.0667,0.0625,0.0714,0.6667,0.6250,0.7143,1.1528,1.1528 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-0,struct_mrx_10_000017,0.8647,0.5421,0.3776,0.2454,0.8176,0.8150,0.3529,0.3529,0.3529,0.4706,0.4706,0.4706,0.8895,0.8895 -0,struct_mrx_10_000018,0.9242,0.5436,0.3764,0.2364,0.9221,0.8322,0.3810,0.3810,0.3810,0.8095,0.8095,0.8095,0.6014,0.5385 -0,struct_mrx_10_000019,0.8516,0.5247,0.3371,0.2146,0.7855,0.8610,0.1818,0.2000,0.1667,0.4091,0.4500,0.3750,4.3428,2.4344 -0,struct_mrx_10_000020,0.7832,0.5464,0.3766,0.2602,0.6817,0.8435,0.2667,0.2400,0.3000,0.5333,0.4800,0.6000,1.6215,1.6145 -0,struct_mrx_10_000021,0.8700,0.4837,0.3346,0.2089,0.8408,0.7247,0.1818,0.1765,0.1875,0.4848,0.4706,0.5000,1.9384,3.0506 -0,struct_mrx_10_000022,0.6893,0.4246,0.3068,0.2072,0.5908,0.7465,0.1600,0.1111,0.2857,0.3600,0.2500,0.6429,0.6894,6.2317 -0,struct_mrx_10_000023,0.7810,0.6403,0.4932,0.3887,0.6748,0.8697,0.3125,0.3125,0.3125,0.4375,0.4375,0.4375,2.6957,2.7436 -0,struct_mrx_10_000024,0.8660,0.6132,0.4524,0.3102,0.8353,0.8119,0.2667,0.2500,0.2857,0.6667,0.6250,0.7143,1.0250,1.0352 -0,struct_mrx_10_000025,0.9048,0.4247,0.2368,0.1370,0.8704,0.7116,0.3415,0.3889,0.3044,0.6829,0.7778,0.6087,0.7581,0.6525 -0,struct_mrx_10_000026,0.9088,0.5720,0.3836,0.2456,0.8758,0.8472,0.3415,0.3684,0.3182,0.7317,0.7895,0.6818,0.7840,0.5859 -0,struct_mrx_10_000027,0.8184,0.4509,0.2700,0.1655,0.7324,0.8035,0.1364,0.1364,0.1364,0.4546,0.4546,0.4546,2.8088,3.2267 -0,struct_mrx_10_000028,0.8552,0.5537,0.3712,0.2411,0.8066,0.8560,0.3044,0.3044,0.3044,0.5652,0.5652,0.5652,0.7484,0.7484 -0,struct_mrx_10_000029,0.8620,0.5935,0.4150,0.2811,0.7929,0.8219,0.1250,0.1250,0.1250,0.5625,0.5625,0.5625,0.9731,0.9702 -0,struct_mrx_10_000030,0.8373,0.4165,0.2456,0.1458,0.7780,0.7061,0.2632,0.2632,0.2632,0.5790,0.5790,0.5790,1.1596,1.1596 -0,struct_mrx_10_000031,0.8386,0.5395,0.3822,0.2534,0.7767,0.8297,0.2632,0.2500,0.2778,0.6316,0.6000,0.6667,0.9570,0.9307 -0,struct_mrx_10_000032,0.8673,0.5864,0.4250,0.2890,0.8028,0.8700,0.4255,0.4000,0.4546,0.6808,0.6400,0.7273,0.5039,0.4383 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-0,struct_mrx_10_000041,0.8202,0.4653,0.2965,0.1835,0.7710,0.7908,0.1778,0.1667,0.1905,0.6222,0.5833,0.6667,1.0715,1.0786 -0,struct_mrx_10_000042,0.8299,0.4465,0.2781,0.1696,0.7716,0.6450,0.2424,0.2500,0.2353,0.6061,0.6250,0.5882,1.2939,1.3084 -0,struct_mrx_10_000043,0.9169,0.4914,0.2759,0.1630,0.8986,0.6912,0.1765,0.2000,0.1579,0.5882,0.6667,0.5263,1.6183,1.0922 -0,struct_mrx_10_000044,0.8906,0.6003,0.4509,0.3046,0.8677,0.8456,0.1143,0.1176,0.1111,0.8571,0.8824,0.8333,1.2064,1.2096 -0,struct_mrx_10_000045,0.8928,0.3777,0.1855,0.1042,0.8430,0.6624,0.0889,0.1000,0.0800,0.4000,0.4500,0.3600,4.8579,1.0277 -0,struct_mrx_10_000046,0.9017,0.6126,0.4710,0.3214,0.8808,0.8787,0.4000,0.4000,0.4000,0.7500,0.7500,0.7500,0.5576,0.4666 -0,struct_mrx_10_000047,0.7944,0.4083,0.2671,0.1636,0.7269,0.7569,0.2000,0.1667,0.2500,0.4400,0.3667,0.5500,0.5961,4.5572 -0,struct_mrx_10_000048,0.8992,0.6383,0.4571,0.3116,0.8581,0.8625,0.3429,0.3750,0.3158,0.5143,0.5625,0.4737,0.7033,0.5788 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-0,struct_mrx_10_000097,0.9038,0.5056,0.2789,0.1667,0.8541,0.7525,0.2703,0.3333,0.2273,0.5946,0.7333,0.5000,2.1079,0.6139 -0,struct_mrx_10_000098,0.8246,0.4508,0.2828,0.1732,0.7711,0.7950,0.2273,0.2273,0.2273,0.7273,0.7273,0.7273,1.7237,1.4020 -0,struct_mrx_10_000099,0.8143,0.4852,0.3157,0.1997,0.7534,0.8116,0.2326,0.2273,0.2381,0.6046,0.5909,0.6190,1.3011,1.2449
--- a/MIREX_data/2010/2010data/MHRAF2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.7991,0.3882,0.3202,0.1996,0.8079,0.7113,0.1667,0.2000,0.1429,0.4167,0.5000,0.3571,3.6515,3.0374 -0,struct_mrx_10_000001,0.9866,0.3305,0.1751,0.0960,0.9950,0.5343,0.3333,0.7143,0.2174,0.4667,1.0000,0.3044,12.9930,0.2871 -0,struct_mrx_10_000002,0.8485,0.4496,0.3121,0.1914,0.8446,0.7858,0.1212,0.1538,0.1000,0.5454,0.6923,0.4500,3.1597,2.3787 -0,struct_mrx_10_000003,0.9171,0.4755,0.3240,0.1969,0.9143,0.7638,0.0714,0.1250,0.0500,0.5000,0.8750,0.3500,3.7701,0.8604 -0,struct_mrx_10_000004,0.9584,0.4780,0.3083,0.1840,0.9518,0.7754,0.1290,0.2222,0.0909,0.4516,0.7778,0.3182,5.2912,1.3551 -0,struct_mrx_10_000005,0.7865,0.3690,0.2494,0.1488,0.7696,0.7277,0.1379,0.2222,0.1000,0.1379,0.2222,0.1000,7.7350,7.0699 -0,struct_mrx_10_000006,0.8848,0.5432,0.4400,0.2914,0.8986,0.8345,0.2581,0.3077,0.2222,0.7097,0.8462,0.6111,1.2909,0.7032 -0,struct_mrx_10_000007,0.9270,0.5186,0.3633,0.2271,0.9074,0.8139,0.1379,0.2222,0.1000,0.4138,0.6667,0.3000,10.4870,1.2492 -0,struct_mrx_10_000008,0.9357,0.2964,0.1796,0.0991,0.9504,0.4864,0.2143,0.4286,0.1429,0.3571,0.7143,0.2381,4.7553,0.8407 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-0,struct_mrx_10_000033,0.9291,0.4920,0.3898,0.2467,0.9290,0.7348,0.4546,0.6250,0.3571,0.6364,0.8750,0.5000,0.8279,0.3812 -0,struct_mrx_10_000034,0.9705,0.3277,0.2096,0.1172,0.9879,0.5680,0.2222,0.4286,0.1500,0.3704,0.7143,0.2500,7.8031,0.9666 -0,struct_mrx_10_000035,0.8069,0.3932,0.2716,0.1647,0.7743,0.6856,0.1379,0.1818,0.1111,0.6207,0.8182,0.5000,2.5245,2.3075 -0,struct_mrx_10_000036,0.7388,0.4440,0.2883,0.1862,0.6384,0.7205,0.1600,0.2000,0.1333,0.1600,0.2000,0.1333,5.4736,6.3486 -0,struct_mrx_10_000037,0.9265,0.4444,0.3201,0.1935,0.9261,0.7167,0.1600,0.2857,0.1111,0.2400,0.4286,0.1667,8.1427,5.4080 -0,struct_mrx_10_000038,0.8795,0.3260,0.2497,0.1448,0.9074,0.5900,0.0870,0.1429,0.0625,0.6087,1.0000,0.4375,2.8927,1.8930 -0,struct_mrx_10_000039,0.9741,0.4410,0.3196,0.1907,0.9860,0.7175,0.4444,0.7500,0.3158,0.5926,1.0000,0.4210,3.3348,0.3075 -0,struct_mrx_10_000040,0.9776,0.2541,0.1908,0.1055,0.9902,0.4800,0.0800,0.2000,0.0500,0.4000,1.0000,0.2500,13.0040,1.0917 -0,struct_mrx_10_000041,0.9892,0.3157,0.1864,0.1029,0.9879,0.5103,0.4667,0.7778,0.3333,0.6000,1.0000,0.4286,1.7009,0.0477 -0,struct_mrx_10_000042,0.9543,0.4809,0.3740,0.2313,0.9768,0.7085,0.4000,0.6250,0.2941,0.6400,1.0000,0.4706,1.3585,0.4356 -0,struct_mrx_10_000043,0.9244,0.4564,0.3028,0.1814,0.9168,0.7248,0.2857,0.4444,0.2105,0.5714,0.8889,0.4210,3.4719,1.2688 -0,struct_mrx_10_000044,0.8248,0.3452,0.2274,0.1325,0.8024,0.6001,0.2222,0.3333,0.1667,0.4444,0.6667,0.3333,5.2055,1.0802 -0,struct_mrx_10_000045,0.9274,0.4805,0.3096,0.1860,0.9230,0.8127,0.1765,0.3333,0.1200,0.3529,0.6667,0.2400,3.9448,1.0826 -0,struct_mrx_10_000046,0.9520,0.3512,0.2354,0.1340,0.9652,0.6113,0.2143,0.3750,0.1500,0.5714,1.0000,0.4000,4.5484,0.5580 -0,struct_mrx_10_000047,0.9118,0.5732,0.4314,0.2832,0.9045,0.8550,0.2500,0.3333,0.2000,0.5000,0.6667,0.4000,3.4860,2.6201 -0,struct_mrx_10_000048,0.8742,0.4567,0.2996,0.1822,0.8428,0.7327,0.2857,0.4444,0.2105,0.3571,0.5556,0.2632,3.6567,0.5185 -0,struct_mrx_10_000049,0.9634,0.5631,0.4652,0.3062,0.9676,0.8098,0.3333,0.4444,0.2667,0.6667,0.8889,0.5333,0.6784,0.5498 -0,struct_mrx_10_000050,0.9424,0.5199,0.3121,0.1875,0.9308,0.7944,0.2632,0.3333,0.2174,0.7368,0.9333,0.6087,1.6194,0.9782 -0,struct_mrx_10_000051,0.8486,0.4312,0.3666,0.2334,0.8542,0.6890,0.0909,0.1111,0.0769,0.4546,0.5556,0.3846,2.2984,2.2984 -0,struct_mrx_10_000052,0.8688,0.3486,0.2507,0.1467,0.8622,0.6070,0.1600,0.2500,0.1176,0.4000,0.6250,0.2941,4.2389,0.7572 -0,struct_mrx_10_000053,0.9707,0.4049,0.2096,0.1175,0.9694,0.6747,0.1212,0.2500,0.0800,0.4242,0.8750,0.2800,7.5256,0.6627 -0,struct_mrx_10_000054,0.8755,0.3647,0.3038,0.1832,0.8906,0.6417,0.1818,0.2857,0.1333,0.5454,0.8571,0.4000,3.3471,0.7327 -0,struct_mrx_10_000055,0.9556,0.5610,0.4999,0.3369,0.9683,0.8276,0.2857,0.4286,0.2143,0.6667,1.0000,0.5000,1.4541,0.8083 -0,struct_mrx_10_000056,0.9470,0.4695,0.3576,0.2199,0.9570,0.7369,0.3077,0.4444,0.2353,0.6154,0.8889,0.4706,1.5029,0.7221 -0,struct_mrx_10_000057,0.9427,0.4566,0.2869,0.1691,0.9466,0.7321,0.2941,0.4546,0.2174,0.5882,0.9091,0.4348,2.1867,0.9026 -0,struct_mrx_10_000058,0.9081,0.4752,0.2870,0.1719,0.8683,0.7568,0.0625,0.1111,0.0435,0.3750,0.6667,0.2609,4.3829,2.2268 -0,struct_mrx_10_000059,0.9498,0.3131,0.2088,0.1170,0.9679,0.5084,0.0769,0.1667,0.0500,0.2308,0.5000,0.1500,8.2563,3.0009 -0,struct_mrx_10_000060,0.9204,0.4864,0.3292,0.2004,0.9198,0.7473,0.2759,0.5000,0.1905,0.2759,0.5000,0.1905,5.2292,1.8717 -0,struct_mrx_10_000061,0.8944,0.3557,0.3032,0.1816,0.9176,0.6056,0.0909,0.1250,0.0714,0.7273,1.0000,0.5714,1.6338,1.4684 -0,struct_mrx_10_000062,0.8144,0.3014,0.2434,0.1422,0.8453,0.5265,0.0833,0.1111,0.0667,0.4167,0.5556,0.3333,3.5390,1.0310 -0,struct_mrx_10_000063,0.9025,0.4877,0.3382,0.2085,0.8948,0.8251,0.3636,0.6000,0.2609,0.3636,0.6000,0.2609,6.8010,0.3846 -0,struct_mrx_10_000064,0.8596,0.4358,0.3126,0.1926,0.8288,0.6050,0.1905,0.2500,0.1538,0.6667,0.8750,0.5385,2.0500,1.8208 -0,struct_mrx_10_000065,0.7434,0.2636,0.2298,0.1354,0.7562,0.5172,0.1739,0.2222,0.1429,0.2609,0.3333,0.2143,5.7802,5.0452 -0,struct_mrx_10_000066,0.8710,0.3886,0.2697,0.1596,0.8684,0.6957,0.2308,0.3750,0.1667,0.3846,0.6250,0.2778,6.7669,1.5150 -0,struct_mrx_10_000067,0.9311,0.2418,0.1712,0.0941,0.9473,0.4392,0.0800,0.1667,0.0526,0.3200,0.6667,0.2105,9.7050,1.3905 -0,struct_mrx_10_000068,0.9147,0.5490,0.4340,0.2862,0.8980,0.7953,0.2400,0.2727,0.2143,0.8000,0.9091,0.7143,0.9055,0.8655 -0,struct_mrx_10_000069,0.9226,0.3746,0.2685,0.1569,0.9322,0.6028,0.4167,0.6250,0.3125,0.5833,0.8750,0.4375,4.2279,0.0722 -0,struct_mrx_10_000070,0.9042,0.3198,0.2988,0.1780,0.9305,0.5908,0.1739,0.2500,0.1333,0.5217,0.7500,0.4000,6.3786,2.0760 -0,struct_mrx_10_000071,0.9695,0.2019,0.1435,0.0774,0.9818,0.3440,0.2308,0.5000,0.1500,0.4615,1.0000,0.3000,15.3740,0.5535 -0,struct_mrx_10_000072,0.7226,0.4000,0.3205,0.2072,0.7081,0.6648,0.3571,0.3333,0.3846,0.6429,0.6000,0.6923,0.8402,1.3905 -0,struct_mrx_10_000073,0.9661,0.3968,0.2842,0.1661,0.9814,0.7171,0.4000,0.6667,0.2857,0.6000,1.0000,0.4286,0.7370,0.3146 -0,struct_mrx_10_000074,0.9220,0.5442,0.4858,0.3279,0.9375,0.7911,0.2105,0.2857,0.1667,0.4210,0.5714,0.3333,3.1502,0.9156 -0,struct_mrx_10_000075,0.8746,0.3909,0.2616,0.1538,0.8759,0.7080,0.2353,0.3077,0.1905,0.5294,0.6923,0.4286,1.8935,1.7181 -0,struct_mrx_10_000076,0.9842,0.2021,0.1883,0.1039,0.9975,0.3184,0.1905,0.4000,0.1250,0.3810,0.8000,0.2500,14.2410,0.6042 -0,struct_mrx_10_000077,0.9519,0.4879,0.3625,0.2233,0.9625,0.7496,0.1667,0.2857,0.1176,0.5833,1.0000,0.4118,3.2585,0.5999 -0,struct_mrx_10_000078,0.8287,0.5280,0.3960,0.2623,0.8080,0.7882,0.0714,0.0833,0.0625,0.5000,0.5833,0.4375,2.9592,2.9592 -0,struct_mrx_10_000079,0.9237,0.4783,0.3341,0.2040,0.9223,0.7396,0.2759,0.3333,0.2353,0.6897,0.8333,0.5882,2.6710,2.5150 -0,struct_mrx_10_000080,0.8721,0.3752,0.2067,0.1177,0.8487,0.6655,0.1176,0.1818,0.0870,0.4118,0.6364,0.3044,4.7145,2.3357 -0,struct_mrx_10_000081,0.9415,0.4090,0.2688,0.1566,0.9499,0.7080,0.2667,0.4444,0.1905,0.6000,1.0000,0.4286,2.3288,1.1000 -0,struct_mrx_10_000082,0.8886,0.4283,0.3426,0.2104,0.9207,0.7287,0.1538,0.2857,0.1053,0.5385,1.0000,0.3684,3.2770,1.1059 -0,struct_mrx_10_000083,0.8118,0.3328,0.2574,0.1556,0.7442,0.5166,0.0909,0.1111,0.0769,0.4546,0.5556,0.3846,2.6564,1.0682 -0,struct_mrx_10_000084,0.8962,0.5337,0.3773,0.2403,0.8783,0.8106,0.1936,0.2500,0.1579,0.6452,0.8333,0.5263,2.2740,1.6900 -0,struct_mrx_10_000085,0.8582,0.5559,0.4157,0.2760,0.8424,0.8106,0.0645,0.0714,0.0588,0.7742,0.8571,0.7059,1.7808,1.8113 -0,struct_mrx_10_000086,0.8629,0.4296,0.2518,0.1491,0.8082,0.6363,0.2143,0.2727,0.1765,0.2857,0.3636,0.2353,5.9884,5.8079 -0,struct_mrx_10_000087,0.9434,0.4620,0.2896,0.1712,0.9393,0.7846,0.2703,0.4546,0.1923,0.5946,1.0000,0.4231,3.1084,0.6238 -0,struct_mrx_10_000088,0.9368,0.4319,0.2984,0.1768,0.9570,0.7193,0.1429,0.2222,0.1053,0.6429,1.0000,0.4737,1.5319,0.6943 -0,struct_mrx_10_000089,0.9276,0.3927,0.2634,0.1533,0.9346,0.7009,0.2759,0.5000,0.1905,0.3448,0.6250,0.2381,3.8523,0.4697 -0,struct_mrx_10_000090,0.8939,0.4548,0.3605,0.2244,0.9151,0.7736,0.2222,0.3000,0.1765,0.6667,0.9000,0.5294,1.5137,0.8242 -0,struct_mrx_10_000091,0.7780,0.3944,0.3620,0.2389,0.7468,0.7260,0.0909,0.1000,0.0833,0.1818,0.2000,0.1667,4.5472,4.5472 -0,struct_mrx_10_000092,0.9708,0.1198,0.1028,0.0543,0.9812,0.1977,0.0667,0.1667,0.0417,0.4000,1.0000,0.2500,9.8904,1.4951 -0,struct_mrx_10_000093,0.8819,0.3508,0.2181,0.1245,0.8818,0.6676,0.1333,0.2500,0.0909,0.2667,0.5000,0.1818,5.4225,3.0846 -0,struct_mrx_10_000094,0.8456,0.4928,0.3389,0.2151,0.7984,0.8046,0.1818,0.3000,0.1304,0.3636,0.6000,0.2609,4.8645,2.5459 -0,struct_mrx_10_000095,0.9761,0.2566,0.2080,0.1161,0.9953,0.3881,0.1739,0.3333,0.1176,0.5217,1.0000,0.3529,1.8236,0.7650 -0,struct_mrx_10_000096,0.9533,0.3532,0.2072,0.1161,0.9631,0.6543,0.1875,0.3750,0.1250,0.5000,1.0000,0.3333,4.2557,0.8528 -0,struct_mrx_10_000097,0.9443,0.2064,0.1381,0.0744,0.9601,0.3285,0.1290,0.2222,0.0909,0.4516,0.7778,0.3182,4.8070,2.1314 -0,struct_mrx_10_000098,0.8700,0.4668,0.2862,0.1724,0.8418,0.7805,0.0571,0.0769,0.0455,0.5714,0.7692,0.4546,2.4359,1.4854 -0,struct_mrx_10_000099,0.9137,0.3268,0.2157,0.1218,0.9436,0.5994,0.2857,0.5714,0.1905,0.4286,0.8571,0.2857,5.2822,0.2320
--- a/MIREX_data/2010/2010data/MND1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8802,0.5941,0.4362,0.2951,0.8364,0.8182,0.5625,0.5000,0.6429,0.6250,0.5556,0.7143,0.0232,0.4585 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-0,struct_mrx_10_000025,0.9251,0.4270,0.2363,0.1356,0.9165,0.6969,0.5263,0.6667,0.4348,0.6842,0.8667,0.5652,0.5023,0.4483 -0,struct_mrx_10_000026,0.8570,0.5680,0.3841,0.2554,0.7748,0.8637,0.1765,0.2500,0.1364,0.4118,0.5833,0.3182,5.6100,1.8228 -0,struct_mrx_10_000027,0.9170,0.4576,0.2644,0.1549,0.9019,0.7488,0.1951,0.2105,0.1818,0.9268,1.0000,0.8636,0.9314,0.9312 -0,struct_mrx_10_000028,0.9190,0.3915,0.2526,0.1461,0.9298,0.7086,0.1379,0.3333,0.0870,0.1379,0.3333,0.0870,8.0408,3.3016 -0,struct_mrx_10_000029,0.9275,0.5783,0.4496,0.2969,0.9258,0.8180,0.1429,0.1667,0.1250,0.7143,0.8333,0.6250,0.5970,0.5936 -0,struct_mrx_10_000030,0.9198,0.4554,0.2954,0.1764,0.9068,0.7336,0.6875,0.8462,0.5790,0.6875,0.8462,0.5790,0.4652,0.4469 -0,struct_mrx_10_000031,0.9054,0.4716,0.2930,0.1754,0.8897,0.7105,0.1482,0.2222,0.1111,0.3704,0.5556,0.2778,5.1060,2.4197 -0,struct_mrx_10_000032,0.8689,0.3664,0.2053,0.1169,0.8458,0.6059,0.1333,0.2500,0.0909,0.2667,0.5000,0.1818,10.6160,2.8769 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-0,struct_mrx_10_000041,0.8759,0.3557,0.1960,0.1111,0.8289,0.6109,0.1290,0.2000,0.0952,0.3871,0.6000,0.2857,7.1493,1.8267 -0,struct_mrx_10_000042,0.9225,0.5841,0.4275,0.2797,0.9065,0.7858,0.5000,0.6364,0.4118,0.7143,0.9091,0.5882,1.8740,0.4218 -0,struct_mrx_10_000043,0.9603,0.4951,0.3378,0.2048,0.9646,0.7523,0.5000,0.7778,0.3684,0.6429,1.0000,0.4737,2.6832,0.0774 -0,struct_mrx_10_000044,0.8946,0.4315,0.2597,0.1541,0.8256,0.6553,0.5333,0.6667,0.4444,0.5333,0.6667,0.4444,2.0286,0.0160 -0,struct_mrx_10_000045,0.8863,0.5096,0.3056,0.1869,0.8377,0.8263,0.1026,0.1429,0.0800,0.4103,0.5714,0.3200,6.4894,1.8122 -0,struct_mrx_10_000046,0.9206,0.3391,0.2219,0.1261,0.9249,0.5986,0.2857,0.5000,0.2000,0.3571,0.6250,0.2500,4.3284,1.6455 -0,struct_mrx_10_000047,0.8395,0.4891,0.2915,0.1800,0.7663,0.7750,0.0851,0.0741,0.1000,0.6808,0.5926,0.8000,1.1704,1.6950 -0,struct_mrx_10_000048,1.0000,0.5922,0.4589,0.2982,0.9955,0.8415,0.6429,1.0000,0.4737,0.6429,1.0000,0.4737,0.0586,0.0134 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-0,struct_mrx_10_000057,0.9567,0.4636,0.2898,0.1705,0.9641,0.7325,0.3750,0.6667,0.2609,0.5625,1.0000,0.3913,3.5028,0.0291 -0,struct_mrx_10_000058,0.9274,0.5159,0.3206,0.1940,0.9230,0.7766,0.3429,0.5000,0.2609,0.6286,0.9167,0.4783,1.7977,0.6822 -0,struct_mrx_10_000059,0.8193,0.4534,0.2611,0.1595,0.7198,0.7277,0.2353,0.2857,0.2000,0.2353,0.2857,0.2000,5.2509,5.2509 -0,struct_mrx_10_000060,0.9550,0.5932,0.4347,0.2818,0.9501,0.8353,0.6842,0.7647,0.6190,0.8421,0.9412,0.7619,0.2791,0.2719 -0,struct_mrx_10_000061,0.9110,0.4200,0.3333,0.2042,0.9064,0.6619,0.6087,0.7778,0.5000,0.6956,0.8889,0.5714,0.6294,0.3932 -0,struct_mrx_10_000062,0.8707,0.4420,0.3442,0.2134,0.8881,0.6950,0.4138,0.4286,0.4000,0.8966,0.9286,0.8667,0.6399,0.8679 -0,struct_mrx_10_000063,0.9089,0.5283,0.3126,0.1913,0.8533,0.8115,0.4000,0.4091,0.3913,0.8444,0.8636,0.8261,0.8296,0.8287 -0,struct_mrx_10_000064,0.8598,0.5484,0.4448,0.3024,0.8411,0.7731,0.1905,0.2500,0.1538,0.5714,0.7500,0.4615,1.4910,0.8896 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-0,struct_mrx_10_000081,0.9271,0.5435,0.3344,0.2051,0.9049,0.7981,0.4571,0.5714,0.3810,0.6857,0.8571,0.5714,1.7222,0.0852 -0,struct_mrx_10_000082,0.8679,0.4622,0.3469,0.2178,0.8516,0.7548,0.1379,0.2000,0.1053,0.4828,0.7000,0.3684,2.6476,1.7915 -0,struct_mrx_10_000083,0.8841,0.5864,0.5257,0.3714,0.8995,0.8180,0.1739,0.2000,0.1538,0.6956,0.8000,0.6154,0.6072,0.6042 -0,struct_mrx_10_000084,0.9087,0.6404,0.4716,0.3227,0.8755,0.8722,0.4242,0.5000,0.3684,0.7879,0.9286,0.6842,0.8804,0.4955 -0,struct_mrx_10_000085,0.9451,0.6619,0.5298,0.3703,0.9304,0.8688,0.6875,0.7333,0.6471,0.8125,0.8667,0.7647,0.0344,0.0294 -0,struct_mrx_10_000086,0.8906,0.4424,0.3011,0.1823,0.8656,0.6954,0.4390,0.3750,0.5294,0.6829,0.5833,0.8235,0.1005,1.2026 -0,struct_mrx_10_000087,0.9673,0.4256,0.2503,0.1438,0.9664,0.7274,0.3429,0.6667,0.2308,0.5143,1.0000,0.3462,6.2109,0.2598 -0,struct_mrx_10_000088,0.9671,0.4969,0.3505,0.2136,0.9766,0.7726,0.6429,1.0000,0.4737,0.6429,1.0000,0.4737,0.4761,0.4611 -0,struct_mrx_10_000089,0.9620,0.4411,0.2902,0.1707,0.9659,0.7284,0.5625,0.8182,0.4286,0.6875,1.0000,0.5238,1.9022,0.4266 -0,struct_mrx_10_000090,0.8957,0.4717,0.3185,0.1944,0.8808,0.7371,0.1600,0.2500,0.1176,0.3200,0.5000,0.2353,7.4853,3.9409 -0,struct_mrx_10_000091,0.9380,0.4627,0.4378,0.2826,0.9711,0.7079,0.2000,0.2500,0.1667,0.8000,1.0000,0.6667,0.5856,0.5791 -0,struct_mrx_10_000092,0.8966,0.4010,0.2286,0.1317,0.8639,0.7267,0.2424,0.4444,0.1667,0.3636,0.6667,0.2500,5.1822,2.2602 -0,struct_mrx_10_000093,0.8364,0.4558,0.2610,0.1568,0.7788,0.7669,0.0952,0.1000,0.0909,0.9048,0.9500,0.8636,1.1044,1.1044 -0,struct_mrx_10_000094,0.9530,0.6305,0.3811,0.2408,0.9128,0.8136,0.6364,0.6667,0.6087,0.6364,0.6667,0.6087,0.2860,0.2860 -0,struct_mrx_10_000095,0.8045,0.5105,0.3455,0.2246,0.7490,0.7695,0.1143,0.1111,0.1176,0.5714,0.5556,0.5882,2.0708,2.0856 -0,struct_mrx_10_000096,0.8909,0.3702,0.2175,0.1241,0.8779,0.7037,0.1212,0.2222,0.0833,0.5454,1.0000,0.3750,6.2688,1.8850 -0,struct_mrx_10_000097,0.9034,0.5276,0.3327,0.2048,0.8847,0.8019,0.3243,0.4000,0.2727,0.8108,1.0000,0.6818,1.8914,0.5423 -0,struct_mrx_10_000098,0.9042,0.4867,0.3026,0.1824,0.8886,0.7865,0.2632,0.3125,0.2273,0.8421,1.0000,0.7273,1.5504,1.5351 -0,struct_mrx_10_000099,1.0000,0.4380,0.2693,0.1557,0.9957,0.6871,0.6875,1.0000,0.5238,0.6875,1.0000,0.5238,0.0600,0.0173
--- a/MIREX_data/2010/2010data/WB1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8865,0.2491,0.2016,0.1137,0.8886,0.4130,0.2857,0.4286,0.2143,0.4762,0.7143,0.3571,8.9629,0.5686 -0,struct_mrx_10_000001,0.9048,0.4238,0.2660,0.1563,0.8908,0.7552,0.1875,0.3333,0.1304,0.3750,0.6667,0.2609,9.2525,1.2071 -0,struct_mrx_10_000002,0.8180,0.2748,0.1839,0.1032,0.8418,0.5658,0.2353,0.2857,0.2000,0.5882,0.7143,0.5000,2.5144,1.6097 -0,struct_mrx_10_000003,0.9390,0.4290,0.2838,0.1668,0.9514,0.7009,0.4000,0.6000,0.3000,0.6667,1.0000,0.5000,1.7685,0.3385 -0,struct_mrx_10_000004,0.8808,0.2776,0.1549,0.0850,0.8762,0.5009,0.2286,0.3077,0.1818,0.5714,0.7692,0.4546,5.8336,1.1408 -0,struct_mrx_10_000005,0.8259,0.3394,0.2537,0.1492,0.8451,0.7076,0.1818,0.2308,0.1500,0.5454,0.6923,0.4500,4.0932,0.9593 -0,struct_mrx_10_000006,0.6456,0.2453,0.1938,0.1140,0.6471,0.6096,0.2500,0.2273,0.2778,0.5000,0.4546,0.5556,2.6122,3.7572 -0,struct_mrx_10_000007,0.9112,0.4262,0.2837,0.1676,0.9227,0.7283,0.2069,0.3333,0.1500,0.5517,0.8889,0.4000,9.1421,2.0918 -0,struct_mrx_10_000008,0.8274,0.3790,0.2316,0.1361,0.7758,0.6950,0.3784,0.4375,0.3333,0.6486,0.7500,0.5714,2.4183,1.4465 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-0,struct_mrx_10_000017,0.8871,0.5212,0.4020,0.2588,0.9005,0.8150,0.3750,0.4000,0.3529,0.8125,0.8667,0.7647,0.8479,0.8462 -0,struct_mrx_10_000018,0.9367,0.4778,0.3286,0.1989,0.9442,0.7887,0.3871,0.6000,0.2857,0.5806,0.9000,0.4286,3.0418,0.2175 -0,struct_mrx_10_000019,0.8839,0.3008,0.1649,0.0911,0.8693,0.6057,0.1143,0.1818,0.0833,0.2857,0.4546,0.2083,6.5246,3.1115 -0,struct_mrx_10_000020,0.9109,0.4929,0.3697,0.2320,0.9093,0.7835,0.4848,0.6154,0.4000,0.7879,1.0000,0.6500,1.4671,0.3625 -0,struct_mrx_10_000021,0.8926,0.4898,0.3740,0.2355,0.9074,0.7469,0.2222,0.2727,0.1875,0.7407,0.9091,0.6250,0.7832,0.5477 -0,struct_mrx_10_000022,0.7754,0.5026,0.4055,0.2808,0.7293,0.7965,0.2222,0.2308,0.2143,0.4444,0.4615,0.4286,4.9228,4.8887 -0,struct_mrx_10_000023,0.9057,0.5655,0.4990,0.3407,0.9320,0.8235,0.2222,0.2727,0.1875,0.8148,1.0000,0.6875,1.7017,1.2098 -0,struct_mrx_10_000024,0.7515,0.3325,0.2296,0.1385,0.6704,0.5828,0.2667,0.2500,0.2857,0.4667,0.4375,0.5000,1.8180,4.4096 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-0,struct_mrx_10_000033,0.8363,0.4742,0.3636,0.2336,0.8206,0.7372,0.4800,0.5454,0.4286,0.5600,0.6364,0.5000,1.4827,0.2802 -0,struct_mrx_10_000034,0.7358,0.3585,0.2425,0.1469,0.6946,0.7498,0.2162,0.2353,0.2000,0.5946,0.6471,0.5500,2.6813,2.4031 -0,struct_mrx_10_000035,0.8446,0.4819,0.3784,0.2418,0.8700,0.7820,0.1875,0.2143,0.1667,0.8750,1.0000,0.7778,1.3351,1.1944 -0,struct_mrx_10_000036,0.8498,0.4976,0.4009,0.2634,0.8392,0.7774,0.4444,0.5000,0.4000,0.7407,0.8333,0.6667,1.3850,0.7839 -0,struct_mrx_10_000037,0.8246,0.4512,0.3416,0.2153,0.8267,0.7728,0.2500,0.2857,0.2222,0.6875,0.7857,0.6111,2.3669,1.7910 -0,struct_mrx_10_000038,0.8897,0.4918,0.3811,0.2426,0.8889,0.7837,0.3077,0.4000,0.2500,0.7692,1.0000,0.6250,2.4164,1.1012 -0,struct_mrx_10_000039,0.8874,0.4122,0.2948,0.1766,0.8918,0.7129,0.2143,0.3333,0.1579,0.3571,0.5556,0.2632,7.2023,2.7087 -0,struct_mrx_10_000040,0.8593,0.5482,0.3850,0.2521,0.8141,0.8397,0.1765,0.2143,0.1500,0.5882,0.7143,0.5000,1.9096,1.5249 -0,struct_mrx_10_000041,0.9098,0.3061,0.1863,0.1036,0.9233,0.5410,0.2069,0.3750,0.1429,0.3448,0.6250,0.2381,6.6662,0.9319 -0,struct_mrx_10_000042,0.8808,0.6643,0.5488,0.4079,0.8387,0.8780,0.4000,0.4615,0.3529,0.6667,0.7692,0.5882,0.7453,0.7192 -0,struct_mrx_10_000043,0.8958,0.3731,0.2538,0.1483,0.8821,0.6616,0.3333,0.4546,0.2632,0.4667,0.6364,0.3684,6.9042,0.7185 -0,struct_mrx_10_000044,0.7760,0.4949,0.3560,0.2397,0.6919,0.8179,0.2857,0.2941,0.2778,0.5714,0.5882,0.5556,2.1542,2.1316 -0,struct_mrx_10_000045,0.8622,0.2712,0.1592,0.0878,0.8454,0.5927,0.3077,0.4286,0.2400,0.4615,0.6429,0.3600,3.7576,1.4007 -0,struct_mrx_10_000046,0.8938,0.4602,0.3323,0.2043,0.8898,0.7784,0.2500,0.3333,0.2000,0.6250,0.8333,0.5000,1.9074,0.8262 -0,struct_mrx_10_000047,0.8981,0.4759,0.3504,0.2169,0.9108,0.7961,0.2941,0.3571,0.2500,0.7647,0.9286,0.6500,1.3063,1.0656 -0,struct_mrx_10_000048,0.8320,0.5145,0.3336,0.2131,0.7673,0.7906,0.3030,0.3571,0.2632,0.5454,0.6429,0.4737,2.7967,1.0757 -0,struct_mrx_10_000049,0.8446,0.3072,0.2787,0.1648,0.9019,0.5988,0.3077,0.3636,0.2667,0.6923,0.8182,0.6000,0.9903,0.8926 -0,struct_mrx_10_000050,0.8565,0.5072,0.3314,0.2081,0.8141,0.8349,0.1429,0.1579,0.1304,0.7143,0.7895,0.6522,1.9213,1.0318 -0,struct_mrx_10_000051,0.6794,0.4985,0.3767,0.2795,0.5775,0.7982,0.2400,0.2500,0.2308,0.3200,0.3333,0.3077,5.2746,5.3058 -0,struct_mrx_10_000052,0.8517,0.4320,0.2823,0.1706,0.8176,0.6872,0.2069,0.2500,0.1765,0.6207,0.7500,0.5294,1.4689,1.0610 -0,struct_mrx_10_000053,0.8247,0.2399,0.1220,0.0664,0.7466,0.5194,0.3333,0.3478,0.3200,0.5833,0.6087,0.5600,1.8953,1.6005 -0,struct_mrx_10_000054,0.8246,0.3902,0.3353,0.2083,0.8592,0.7031,0.2400,0.3000,0.2000,0.4800,0.6000,0.4000,4.2465,1.5408 -0,struct_mrx_10_000055,0.8216,0.4563,0.3392,0.2162,0.7867,0.7260,0.2308,0.2500,0.2143,0.6154,0.6667,0.5714,2.5876,1.8018 -0,struct_mrx_10_000056,0.8816,0.5626,0.4479,0.3000,0.8829,0.8327,0.4375,0.4667,0.4118,0.9375,1.0000,0.8824,1.4361,0.8888 -0,struct_mrx_10_000057,0.8527,0.4224,0.2612,0.1554,0.8182,0.7361,0.3077,0.3750,0.2609,0.5641,0.6875,0.4783,3.0627,0.9891 -0,struct_mrx_10_000058,0.9064,0.5202,0.3614,0.2255,0.9095,0.8176,0.5128,0.6250,0.4348,0.7692,0.9375,0.6522,1.1293,0.2703 -0,struct_mrx_10_000059,0.7779,0.3487,0.2608,0.1580,0.7466,0.7163,0.2222,0.2500,0.2000,0.4444,0.5000,0.4000,3.1909,1.3319 -0,struct_mrx_10_000060,0.8485,0.4208,0.3061,0.1862,0.8588,0.7381,0.1875,0.2727,0.1429,0.5625,0.8182,0.4286,3.2199,1.1948 -0,struct_mrx_10_000061,0.9034,0.6332,0.5556,0.4012,0.9035,0.8654,0.5385,0.5833,0.5000,0.9231,1.0000,0.8571,0.4999,0.3567 -0,struct_mrx_10_000062,0.6361,0.4146,0.3314,0.2299,0.5938,0.7841,0.2286,0.2000,0.2667,0.5714,0.5000,0.6667,0.7659,2.1663 -0,struct_mrx_10_000063,0.8972,0.4185,0.2667,0.1564,0.9064,0.7523,0.3333,0.4615,0.2609,0.6111,0.8462,0.4783,3.0449,0.8862 -0,struct_mrx_10_000064,0.8512,0.6789,0.6019,0.4801,0.8065,0.8842,0.3200,0.3333,0.3077,0.8000,0.8333,0.7692,0.8388,0.8091 -0,struct_mrx_10_000065,0.7744,0.5104,0.3979,0.2759,0.7134,0.7947,0.2963,0.3077,0.2857,0.6667,0.6923,0.6429,2.0685,2.0054 -0,struct_mrx_10_000066,0.7411,0.3110,0.2274,0.1340,0.7482,0.6715,0.1936,0.2308,0.1667,0.3226,0.3846,0.2778,3.5761,3.4407 -0,struct_mrx_10_000067,0.9092,0.5470,0.4094,0.2642,0.9090,0.8386,0.2759,0.4000,0.2105,0.6207,0.9000,0.4737,2.6420,1.5435 -0,struct_mrx_10_000068,0.8094,0.5720,0.4490,0.3189,0.7588,0.8362,0.2308,0.2500,0.2143,0.4615,0.5000,0.4286,4.3700,3.3434 -0,struct_mrx_10_000069,0.8598,0.5548,0.4219,0.2843,0.8175,0.8243,0.3333,0.3571,0.3125,0.7333,0.7857,0.6875,1.1162,1.0258 -0,struct_mrx_10_000070,0.7793,0.5216,0.3817,0.2616,0.7059,0.7861,0.2667,0.2667,0.2667,0.4667,0.4667,0.4667,3.4076,3.4076 -0,struct_mrx_10_000071,0.8971,0.2452,0.1798,0.0996,0.9254,0.5270,0.2222,0.4286,0.1500,0.2963,0.5714,0.2000,13.7640,2.2504 -0,struct_mrx_10_000072,0.8228,0.4907,0.3901,0.2551,0.8286,0.7089,0.2400,0.2500,0.2308,0.8000,0.8333,0.7692,1.8076,1.8126 -0,struct_mrx_10_000073,0.7721,0.3273,0.2282,0.1331,0.7971,0.6890,0.3784,0.4375,0.3333,0.4324,0.5000,0.3810,3.7764,2.7252 -0,struct_mrx_10_000074,0.8033,0.5125,0.4476,0.3117,0.7934,0.7933,0.4348,0.4546,0.4167,0.5217,0.5454,0.5000,2.7182,1.1453 -0,struct_mrx_10_000075,0.8871,0.4132,0.2644,0.1561,0.8653,0.7156,0.4118,0.5385,0.3333,0.5294,0.6923,0.4286,4.2381,0.3505 -0,struct_mrx_10_000076,0.9155,0.3887,0.3003,0.1794,0.9192,0.6609,0.3333,0.5000,0.2500,0.5833,0.8750,0.4375,4.4697,0.4199 -0,struct_mrx_10_000077,0.8678,0.5432,0.4345,0.2911,0.8565,0.8335,0.2667,0.3077,0.2353,0.5333,0.6154,0.4706,3.1924,1.6030 -0,struct_mrx_10_000078,0.8433,0.5060,0.3669,0.2396,0.7833,0.7679,0.3704,0.4546,0.3125,0.3704,0.4546,0.3125,11.0440,3.6100 -0,struct_mrx_10_000079,0.7923,0.4258,0.3000,0.1885,0.7350,0.7561,0.2667,0.3077,0.2353,0.4000,0.4615,0.3529,7.2050,3.3060 -0,struct_mrx_10_000080,0.8671,0.3901,0.2465,0.1437,0.8663,0.7265,0.4000,0.4706,0.3478,0.6000,0.7059,0.5217,1.7228,0.6261 -0,struct_mrx_10_000081,0.8228,0.4406,0.3061,0.1885,0.8135,0.7921,0.2222,0.2667,0.1905,0.8333,1.0000,0.7143,2.1882,1.5430 -0,struct_mrx_10_000082,0.8722,0.4114,0.2878,0.1722,0.8779,0.6656,0.2000,0.2727,0.1579,0.7333,1.0000,0.5790,1.8823,0.8270 -0,struct_mrx_10_000083,0.7902,0.5884,0.4991,0.3792,0.7300,0.8341,0.3478,0.4000,0.3077,0.4348,0.5000,0.3846,4.5402,3.3255 -0,struct_mrx_10_000084,0.8880,0.4891,0.3599,0.2259,0.8847,0.7953,0.3226,0.4167,0.2632,0.7097,0.9167,0.5790,1.9937,0.6354 -0,struct_mrx_10_000085,0.7005,0.2959,0.2136,0.1286,0.6319,0.6286,0.1714,0.1667,0.1765,0.3429,0.3333,0.3529,3.3550,4.2433 -0,struct_mrx_10_000086,0.7593,0.2718,0.2226,0.1305,0.7591,0.5969,0.2353,0.2353,0.2353,0.5294,0.5294,0.5294,2.9873,2.9873 -0,struct_mrx_10_000087,0.8425,0.4366,0.2560,0.1529,0.7846,0.7853,0.2500,0.3571,0.1923,0.4500,0.6429,0.3462,5.2646,2.0033 -0,struct_mrx_10_000088,0.8612,0.3998,0.2609,0.1543,0.8423,0.7022,0.2000,0.2727,0.1579,0.4667,0.6364,0.3684,4.3554,1.7014 -0,struct_mrx_10_000089,0.8844,0.4027,0.2548,0.1492,0.8717,0.7086,0.3429,0.4286,0.2857,0.5714,0.7143,0.4762,2.9141,1.4145 -0,struct_mrx_10_000090,0.8472,0.4524,0.3443,0.2171,0.8312,0.7810,0.2143,0.2727,0.1765,0.5714,0.7273,0.4706,4.8784,1.5263 -0,struct_mrx_10_000091,0.7959,0.5270,0.4373,0.3072,0.7585,0.7706,0.4348,0.4546,0.4167,0.6956,0.7273,0.6667,1.3731,0.9334 -0,struct_mrx_10_000092,0.9293,0.3347,0.1893,0.1052,0.9411,0.6207,0.2857,0.4546,0.2083,0.6286,1.0000,0.4583,2.7353,0.7167 -0,struct_mrx_10_000093,0.8764,0.5334,0.3516,0.2240,0.8172,0.8444,0.3429,0.4615,0.2727,0.4571,0.6154,0.3636,3.2783,0.6847 -0,struct_mrx_10_000094,0.8199,0.3217,0.2022,0.1159,0.7925,0.6080,0.1818,0.3000,0.1304,0.3636,0.6000,0.2609,9.8758,2.2115 -0,struct_mrx_10_000095,0.8655,0.5362,0.4372,0.2914,0.8753,0.8162,0.1875,0.2000,0.1765,0.8750,0.9333,0.8235,1.0098,1.0098 -0,struct_mrx_10_000096,0.8854,0.3004,0.1531,0.0840,0.8613,0.5538,0.2105,0.2857,0.1667,0.5790,0.7857,0.4583,3.0986,0.9029 -0,struct_mrx_10_000097,0.8582,0.4578,0.2955,0.1797,0.8314,0.7766,0.2222,0.2857,0.1818,0.6667,0.8571,0.5454,2.3859,1.6353 -0,struct_mrx_10_000098,0.8900,0.5018,0.3330,0.2056,0.8759,0.8174,0.2353,0.3333,0.1818,0.5882,0.8333,0.4546,2.9438,1.5946 -0,struct_mrx_10_000099,0.7204,0.3844,0.2436,0.1509,0.6328,0.7719,0.2105,0.2353,0.1905,0.2632,0.2941,0.2381,4.5654,4.5654
--- a/MIREX_data/2010/2010data2/BV1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/BV1\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2010/2010data2/BV2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/BV2\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2010/2010data2/GP7-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/GP7\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2010/2010data2/MHRAF2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/MHRAF2\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2010/2010data2/MND1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/MND1\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2010/2010data2/WB1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN"> -<html><head> -<title>404 Not Found</title> -</head><body> -<h1>Not Found</h1> -<p>The requested URL /nema_out/mirex2010/results/struct/mirex10_2/WB1\per_track_results.csv was not found on this server.</p> -<hr> -<address>Apache/2.2.3 (CentOS) Server at www.music-ir.org Port 80</address> -</body></html>
--- a/MIREX_data/2011/2009data/CL1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.7910,0.4359,0.5714,0.4295,0.8537,0.5653,0.2105,0.2857,0.1667,0.3158,0.4286,0.2500,9.3533,1.2878 -0,struct_mrx_09_000001,0.7643,0.4460,0.5278,0.3951,0.7948,0.7183,0.1250,0.1429,0.1111,0.6250,0.7143,0.5556,2.9832,2.6189 -0,struct_mrx_09_000002,0.5127,0.5635,0.4694,0.5492,0.4098,0.7327,0.1538,0.1333,0.1818,0.6154,0.5333,0.7273,1.6633,2.0190 -0,struct_mrx_09_000003,0.4756,0.3362,0.3755,0.3365,0.4247,0.6580,0.0952,0.1000,0.0909,0.3810,0.4000,0.3636,4.1284,3.3596 -0,struct_mrx_09_000004,0.7620,0.5515,0.5709,0.4534,0.7706,0.7167,0.0870,0.1000,0.0769,0.4348,0.5000,0.3846,5.8954,1.3620 -0,struct_mrx_09_000005,0.5704,0.2277,0.5126,0.4261,0.6432,0.5875,0.4000,0.3333,0.5000,0.4000,0.3333,0.5000,4.8863,10.4560 -0,struct_mrx_09_000006,0.6355,0.6474,0.6280,0.6613,0.5979,0.7737,0.1250,0.1250,0.1250,0.2500,0.2500,0.2500,3.4841,3.3468 -0,struct_mrx_09_000007,0.8009,0.5529,0.5049,0.3767,0.7654,0.7572,0.1053,0.1429,0.0833,0.3158,0.4286,0.2500,11.4320,6.4261 -0,struct_mrx_09_000008,0.8711,0.4706,0.5596,0.4118,0.8726,0.6375,0.2857,0.2857,0.2857,0.4286,0.4286,0.4286,3.4971,1.6113 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-0,struct_mrx_09_000017,0.5820,0.3461,0.4473,0.3681,0.5699,0.6703,0.0870,0.0833,0.0909,0.2609,0.2500,0.2727,6.7041,5.4666 -0,struct_mrx_09_000018,0.8111,0.4170,0.4101,0.2812,0.7574,0.6457,0.0909,0.1250,0.0714,0.2727,0.3750,0.2143,16.8080,2.1410 -0,struct_mrx_09_000019,0.5557,0.4927,0.4402,0.4242,0.4575,0.6688,0.1600,0.1429,0.1818,0.4800,0.4286,0.5454,2.4461,2.9139 -0,struct_mrx_09_000020,0.7788,0.6793,0.7050,0.6282,0.8030,0.8238,0.1905,0.1818,0.2000,0.3810,0.3636,0.4000,3.2183,2.5750 -0,struct_mrx_09_000021,0.6077,0.2966,0.3187,0.2155,0.6118,0.6158,0.2143,0.2143,0.2143,0.3571,0.3571,0.3571,8.2609,6.2323 -0,struct_mrx_09_000022,0.6453,0.4377,0.4698,0.3844,0.6041,0.7265,0.0769,0.1000,0.0625,0.3846,0.5000,0.3125,9.0746,2.6737 -0,struct_mrx_09_000023,0.5740,0.3847,0.4858,0.4063,0.6041,0.6913,0.3000,0.3333,0.2727,0.4000,0.4444,0.3636,3.9266,3.2061 -0,struct_mrx_09_000024,0.5800,0.5586,0.6121,0.6135,0.6107,0.7986,0.2069,0.1667,0.2727,0.5517,0.4444,0.7273,1.3115,2.5000 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--- a/MIREX_data/2011/2009data/GP6-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.4527,0.7929,0.4282,0.7970,0.2927,0.7348,0.0714,0.0625,0.0833,0.5000,0.4375,0.5833,1.8173,3.3311 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-0,struct_mrx_09_000025,0.7095,0.8028,0.6478,0.6938,0.6074,0.8765,0.5217,0.4615,0.6000,0.6956,0.6154,0.8000,0.4469,0.9924 -0,struct_mrx_09_000026,0.3820,0.8339,0.4207,0.8673,0.2777,0.6496,0.1176,0.0833,0.2000,0.3529,0.2500,0.6000,1.1042,6.5647 -0,struct_mrx_09_000027,0.5043,0.8328,0.4577,0.7611,0.3273,0.7240,0.4000,0.2941,0.6250,0.5600,0.4118,0.8750,0.4167,3.7513 -0,struct_mrx_09_000028,0.5281,0.8198,0.4640,0.7716,0.3317,0.7917,0.2727,0.2308,0.3333,0.5454,0.4615,0.6667,2.3775,2.4253 -0,struct_mrx_09_000029,0.7812,0.8070,0.7476,0.7470,0.7481,0.8993,0.0769,0.0588,0.1111,0.4615,0.3529,0.6667,1.3179,1.8293 -0,struct_mrx_09_000030,0.5700,0.6276,0.4297,0.4678,0.3974,0.7408,0.2222,0.1667,0.3333,0.2963,0.2222,0.4444,4.8211,5.8346 -0,struct_mrx_09_000031,0.4900,0.9121,0.4688,0.9399,0.3122,0.7114,0.1875,0.1304,0.3333,0.5625,0.3913,1.0000,0.7223,1.3528 -0,struct_mrx_09_000032,0.4473,0.7895,0.4005,0.7364,0.2751,0.7133,0.1600,0.1250,0.2222,0.4800,0.3750,0.6667,1.3815,2.1708 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--- a/MIREX_data/2011/2009data/MHRAF1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.5640,0.6370,0.5474,0.6136,0.4941,0.7294,0.3200,0.3077,0.3333,0.4800,0.4615,0.5000,3.0635,1.0928 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--- a/MIREX_data/2011/2009data/MHRAF2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.4981,0.6386,0.4973,0.6141,0.4178,0.7182,0.1600,0.1538,0.1667,0.4800,0.4615,0.5000,3.1337,1.2331 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--- a/MIREX_data/2011/2009data/MHRAF3-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.4827,0.5630,0.4728,0.5183,0.4347,0.6745,0.3636,0.4000,0.3333,0.6364,0.7000,0.5833,1.6012,0.8982 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--- a/MIREX_data/2011/2009data/SBVRS1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,298 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_09_000000,0.4364,0.6602,0.4370,0.6028,0.3427,0.7017,0.1667,0.1667,0.1667,0.6667,0.6667,0.6667,1.9263,1.9263 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-0,struct_mrx_09_000273,0.4224,0.5011,0.4473,0.5007,0.4041,0.6905,0.0833,0.1000,0.0714,0.6667,0.8000,0.5714,2.0064,1.9936 -0,struct_mrx_09_000274,0.3812,0.7712,0.4225,0.8007,0.2869,0.6609,0.4706,0.5000,0.4444,0.8235,0.8750,0.7778,1.9308,1.1794 -0,struct_mrx_09_000275,0.6041,0.6583,0.5239,0.5554,0.4958,0.7874,0.0800,0.0769,0.0833,0.8000,0.7692,0.8333,1.2747,1.2752 -0,struct_mrx_09_000276,0.5009,0.4515,0.5920,0.5631,0.6240,0.6559,0.2857,0.3333,0.2500,0.8571,1.0000,0.7500,2.0173,1.3543 -0,struct_mrx_09_000277,0.6804,0.7745,0.7618,0.8378,0.6984,0.8476,0.1667,0.1250,0.2500,0.5000,0.3750,0.7500,1.2991,4.3003 -0,struct_mrx_09_000278,0.4739,0.5686,0.4112,0.4825,0.3582,0.7308,0.2353,0.2500,0.2222,0.4706,0.5000,0.4444,3.0559,3.0082 -0,struct_mrx_09_000279,0.4461,0.8038,0.3879,0.8294,0.2532,0.7600,0.2353,0.1600,0.4444,0.4118,0.2800,0.7778,0.6827,5.8172 -0,struct_mrx_09_000280,0.9937,0.5071,0.7244,0.5747,0.9796,0.7569,0.8235,0.7778,0.8750,0.9412,0.8889,1.0000,0.0581,0.0639 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-0,struct_mrx_09_000289,0.7103,0.8336,0.7330,0.8296,0.6566,0.8903,0.1429,0.1176,0.1818,0.7857,0.6471,1.0000,1.2401,1.7618 -0,struct_mrx_09_000290,0.7804,0.8139,0.7366,0.7491,0.7244,0.9172,0.4375,0.3333,0.6364,0.5625,0.4286,0.8182,0.0754,3.6729 -0,struct_mrx_09_000291,0.4146,0.3618,0.4842,0.4930,0.4758,0.6407,0.0000,0.0000,0.0000,0.4167,0.2778,0.8333,0.9381,7.7179 -0,struct_mrx_09_000292,0.4859,0.6002,0.4534,0.5638,0.3792,0.6747,0.2609,0.2500,0.2727,0.6087,0.5833,0.6364,1.8864,1.8981 -0,struct_mrx_09_000293,0.6340,0.7666,0.6225,0.7561,0.5290,0.7841,0.3077,0.2500,0.4000,0.6923,0.5625,0.9000,1.1233,1.7468 -0,struct_mrx_09_000294,0.5136,0.7116,0.4338,0.6653,0.3218,0.8309,0.0000,0.0000,0.0000,0.3684,0.2593,0.6364,2.9664,5.8860 -0,struct_mrx_09_000295,0.6470,0.4642,0.5246,0.4259,0.6828,0.7270,0.0000,0.0000,0.0000,0.5333,0.5714,0.5000,2.8767,2.4097 -0,struct_mrx_09_000296,0.4325,0.5097,0.4160,0.4633,0.3774,0.6534,0.2727,0.3333,0.2308,0.7273,0.8889,0.6154,2.1039,0.9752
--- a/MIREX_data/2011/2010data/CL1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.7948,0.3949,0.2876,0.1779,0.7510,0.6876,0.3077,0.3333,0.2857,0.3846,0.4167,0.3571,4.0644,2.9174 -0,struct_mrx_10_000001,0.9270,0.2931,0.1588,0.0869,0.9144,0.5207,0.1936,0.3750,0.1304,0.3871,0.7500,0.2609,21.5770,1.5061 -0,struct_mrx_10_000002,0.7768,0.4279,0.2962,0.1862,0.7239,0.7994,0.2222,0.2500,0.2000,0.3889,0.4375,0.3500,4.7838,3.3966 -0,struct_mrx_10_000003,0.9171,0.3526,0.2302,0.1318,0.9107,0.6213,0.2222,0.4286,0.1500,0.2963,0.5714,0.2000,8.5577,1.3971 -0,struct_mrx_10_000004,0.8618,0.4342,0.2677,0.1599,0.8224,0.7647,0.1765,0.2500,0.1364,0.4118,0.5833,0.3182,8.3892,2.1276 -0,struct_mrx_10_000005,0.8374,0.3622,0.2361,0.1378,0.8218,0.6887,0.2424,0.3077,0.2000,0.5454,0.6923,0.4500,5.3840,1.7922 -0,struct_mrx_10_000006,0.8421,0.4174,0.2751,0.1654,0.8173,0.6902,0.2581,0.3077,0.2222,0.5161,0.6154,0.4444,5.0603,1.7975 -0,struct_mrx_10_000007,0.8951,0.3350,0.1948,0.1097,0.8670,0.5801,0.2143,0.3750,0.1500,0.3571,0.6250,0.2500,14.0170,1.9300 -0,struct_mrx_10_000008,0.8161,0.4404,0.2898,0.1797,0.7487,0.7824,0.1714,0.2143,0.1429,0.5143,0.6429,0.4286,4.2762,1.5349 -0,struct_mrx_10_000009,0.8577,0.2786,0.1826,0.1019,0.8762,0.5894,0.1818,0.2727,0.1364,0.3030,0.4546,0.2273,8.4556,3.3701 -0,struct_mrx_10_000010,0.8477,0.4478,0.3090,0.1904,0.8192,0.7670,0.2000,0.2500,0.1667,0.4000,0.5000,0.3333,5.3054,2.0496 -0,struct_mrx_10_000011,0.7274,0.3940,0.3083,0.1990,0.6842,0.7501,0.2222,0.2500,0.2000,0.2222,0.2500,0.2000,4.6734,4.1693 -0,struct_mrx_10_000012,0.8744,0.3140,0.1805,0.1008,0.8593,0.5632,0.2500,0.3333,0.2000,0.4375,0.5833,0.3500,8.8235,2.0828 -0,struct_mrx_10_000013,0.9512,0.2275,0.1405,0.0759,0.9496,0.3096,0.2667,0.5714,0.1739,0.4000,0.8571,0.2609,51.5300,0.1209 -0,struct_mrx_10_000014,0.8827,0.2528,0.1553,0.0853,0.8616,0.4394,0.2000,0.3333,0.1429,0.4000,0.6667,0.2857,8.4775,1.6954 -0,struct_mrx_10_000015,0.8488,0.3719,0.2842,0.1704,0.8564,0.7204,0.2143,0.3333,0.1579,0.4286,0.6667,0.3158,5.3932,2.0167 -0,struct_mrx_10_000016,0.7660,0.4169,0.3039,0.1913,0.7390,0.7866,0.1053,0.1053,0.1053,0.7368,0.7368,0.7368,2.0864,2.0864 -0,struct_mrx_10_000017,0.7669,0.3979,0.3049,0.1916,0.7459,0.7678,0.1936,0.2143,0.1765,0.3871,0.4286,0.3529,4.2719,3.3876 -0,struct_mrx_10_000018,0.8602,0.3636,0.2436,0.1422,0.8494,0.7103,0.2500,0.3636,0.1905,0.3750,0.5454,0.2857,5.7515,2.3378 -0,struct_mrx_10_000019,0.8567,0.3686,0.2223,0.1284,0.8297,0.7403,0.2162,0.3077,0.1667,0.2703,0.3846,0.2083,6.4638,3.7444 -0,struct_mrx_10_000020,0.7362,0.3635,0.2530,0.1558,0.6720,0.7234,0.1714,0.2000,0.1500,0.3429,0.4000,0.3000,5.9287,3.8848 -0,struct_mrx_10_000021,0.8406,0.3744,0.2951,0.1785,0.8494,0.6632,0.3200,0.4444,0.2500,0.3200,0.4444,0.2500,4.6012,2.0661 -0,struct_mrx_10_000022,0.6889,0.3076,0.2402,0.1471,0.6556,0.6064,0.2222,0.2308,0.2143,0.3704,0.3846,0.3571,7.4658,6.7926 -0,struct_mrx_10_000023,0.8274,0.2986,0.2280,0.1327,0.8076,0.4839,0.2308,0.3000,0.1875,0.4615,0.6000,0.3750,6.0371,1.6970 -0,struct_mrx_10_000024,0.8165,0.4483,0.3268,0.2068,0.7796,0.7002,0.3200,0.3636,0.2857,0.4000,0.4546,0.3571,4.7299,2.4310 -0,struct_mrx_10_000025,0.8544,0.3459,0.1992,0.1130,0.8390,0.6546,0.1714,0.2500,0.1304,0.4000,0.5833,0.3044,6.3944,2.3613 -0,struct_mrx_10_000026,0.9422,0.3312,0.2048,0.1148,0.9470,0.6010,0.3226,0.5556,0.2273,0.4516,0.7778,0.3182,9.1582,0.4624 -0,struct_mrx_10_000027,0.8546,0.4008,0.2394,0.1399,0.8290,0.7354,0.1667,0.2143,0.1364,0.3889,0.5000,0.3182,4.8389,2.3376 -0,struct_mrx_10_000028,0.8620,0.2913,0.1801,0.1008,0.8446,0.5953,0.2941,0.4546,0.2174,0.3529,0.5454,0.2609,7.3274,0.3636 -0,struct_mrx_10_000029,0.7853,0.4272,0.3275,0.2105,0.7373,0.7585,0.2500,0.2500,0.2500,0.5625,0.5625,0.5625,2.3950,1.8055 -0,struct_mrx_10_000030,0.8008,0.3063,0.2280,0.1326,0.8139,0.6612,0.1875,0.2308,0.1579,0.5000,0.6154,0.4210,3.8513,1.1690 -0,struct_mrx_10_000031,0.9141,0.3117,0.1997,0.1122,0.9068,0.5090,0.2400,0.4286,0.1667,0.3200,0.5714,0.2222,11.9280,1.0010 -0,struct_mrx_10_000032,0.7808,0.4202,0.2526,0.1537,0.7086,0.7448,0.2222,0.2857,0.1818,0.2778,0.3571,0.2273,4.8680,4.7472 -0,struct_mrx_10_000033,0.7457,0.3602,0.3114,0.1964,0.7522,0.6954,0.3200,0.3636,0.2857,0.4800,0.5454,0.4286,3.9089,1.4932 -0,struct_mrx_10_000034,0.7823,0.2876,0.2117,0.1224,0.7833,0.6643,0.2353,0.2857,0.2000,0.4706,0.5714,0.4000,5.6919,2.2892 -0,struct_mrx_10_000035,0.8298,0.4531,0.3296,0.2064,0.8185,0.7475,0.2581,0.3077,0.2222,0.5161,0.6154,0.4444,2.7689,2.5422 -0,struct_mrx_10_000036,0.8332,0.3381,0.2529,0.1501,0.8033,0.5773,0.2963,0.3333,0.2667,0.4444,0.5000,0.4000,3.5366,2.0000 -0,struct_mrx_10_000037,0.8710,0.2652,0.1867,0.1045,0.8744,0.4564,0.2222,0.3333,0.1667,0.4444,0.6667,0.3333,6.9928,1.7067 -0,struct_mrx_10_000038,0.7834,0.3404,0.2782,0.1688,0.7896,0.6922,0.2759,0.3077,0.2500,0.4828,0.5385,0.4375,4.4955,2.4916 -0,struct_mrx_10_000039,0.9774,0.2648,0.1757,0.0965,0.9788,0.3852,0.3077,0.5714,0.2105,0.4615,0.8571,0.3158,14.6690,0.1355 -0,struct_mrx_10_000040,0.8149,0.4738,0.3420,0.2186,0.7858,0.8124,0.1714,0.2000,0.1500,0.5714,0.6667,0.5000,2.4909,2.0450 -0,struct_mrx_10_000041,0.8095,0.3194,0.2231,0.1291,0.8204,0.6725,0.1818,0.2500,0.1429,0.4242,0.5833,0.3333,4.7684,1.9488 -0,struct_mrx_10_000042,0.7815,0.4216,0.3011,0.1895,0.7332,0.6956,0.2222,0.3000,0.1765,0.3704,0.5000,0.2941,9.3926,3.7221 -0,struct_mrx_10_000043,0.9286,0.4555,0.2893,0.1718,0.9158,0.7078,0.2222,0.3750,0.1579,0.4444,0.7500,0.3158,10.1460,1.4481 -0,struct_mrx_10_000044,0.7809,0.2807,0.2035,0.1174,0.7649,0.5628,0.2143,0.3000,0.1667,0.4286,0.6000,0.3333,6.8528,2.4251 -0,struct_mrx_10_000045,0.9150,0.2584,0.1345,0.0725,0.9198,0.4583,0.2162,0.3333,0.1600,0.5405,0.8333,0.4000,4.8635,1.0807 -0,struct_mrx_10_000046,0.7705,0.4443,0.3099,0.1982,0.7094,0.8032,0.2162,0.2353,0.2000,0.5946,0.6471,0.5500,2.3444,1.5598 -0,struct_mrx_10_000047,0.8245,0.3962,0.2721,0.1634,0.8145,0.7358,0.2424,0.3077,0.2000,0.4242,0.5385,0.3500,6.0617,2.9447 -0,struct_mrx_10_000048,0.7889,0.4106,0.2890,0.1796,0.7401,0.7527,0.2000,0.2727,0.1579,0.2667,0.3636,0.2105,5.4385,3.4380 -0,struct_mrx_10_000049,0.8084,0.3773,0.2756,0.1672,0.7845,0.6437,0.2963,0.3333,0.2667,0.3704,0.4167,0.3333,4.0818,3.3183 -0,struct_mrx_10_000050,0.8552,0.3330,0.2151,0.1232,0.8447,0.6907,0.2703,0.3571,0.2174,0.4865,0.6429,0.3913,4.2135,1.6467 -0,struct_mrx_10_000051,0.6116,0.4074,0.3368,0.2445,0.5410,0.7764,0.1875,0.1579,0.2308,0.5000,0.4210,0.6154,2.5445,4.4788 -0,struct_mrx_10_000052,0.8328,0.2950,0.2095,0.1203,0.8081,0.5382,0.2143,0.2727,0.1765,0.5000,0.6364,0.4118,5.2729,0.9979 -0,struct_mrx_10_000053,0.9020,0.2765,0.1609,0.0883,0.9057,0.5781,0.2353,0.4444,0.1600,0.2941,0.5556,0.2000,10.7280,2.0474 -0,struct_mrx_10_000054,0.8141,0.4107,0.3198,0.1989,0.8157,0.6968,0.2759,0.2857,0.2667,0.6207,0.6429,0.6000,1.8413,1.7431 -0,struct_mrx_10_000055,0.9059,0.3631,0.2876,0.1708,0.9107,0.5994,0.2000,0.3333,0.1429,0.4000,0.6667,0.2857,11.4430,0.7232 -0,struct_mrx_10_000056,0.7410,0.4523,0.3378,0.2252,0.6756,0.7966,0.1875,0.2000,0.1765,0.4375,0.4667,0.4118,3.9971,3.1727 -0,struct_mrx_10_000057,0.8794,0.4006,0.2551,0.1490,0.8855,0.7049,0.1714,0.2500,0.1304,0.4571,0.6667,0.3478,3.7948,1.2204 -0,struct_mrx_10_000058,0.7942,0.4738,0.2825,0.1778,0.6864,0.8029,0.2105,0.2667,0.1739,0.3684,0.4667,0.3044,4.4599,3.2695 -0,struct_mrx_10_000059,0.7186,0.3722,0.2413,0.1493,0.6289,0.7349,0.1622,0.1765,0.1500,0.3243,0.3529,0.3000,5.5196,4.1877 -0,struct_mrx_10_000060,0.7415,0.3512,0.2257,0.1356,0.6726,0.6897,0.1765,0.2308,0.1429,0.3529,0.4615,0.2857,5.9955,5.0165 -0,struct_mrx_10_000061,0.7479,0.4452,0.3650,0.2449,0.7163,0.7670,0.2400,0.2727,0.2143,0.4000,0.4546,0.3571,3.5036,3.2553 -0,struct_mrx_10_000062,0.7446,0.3841,0.2974,0.1884,0.7052,0.6973,0.3448,0.3571,0.3333,0.4828,0.5000,0.4667,3.2022,2.0271 -0,struct_mrx_10_000063,0.9550,0.2543,0.1482,0.0803,0.9644,0.4493,0.2000,0.4286,0.1304,0.4000,0.8571,0.2609,13.8550,0.9748 -0,struct_mrx_10_000064,0.9174,0.3413,0.2796,0.1651,0.9112,0.4898,0.3158,0.5000,0.2308,0.5263,0.8333,0.3846,7.3950,0.5181 -0,struct_mrx_10_000065,0.7181,0.3312,0.2599,0.1602,0.6874,0.6278,0.2222,0.2308,0.2143,0.4444,0.4615,0.4286,4.0875,3.3651 -0,struct_mrx_10_000066,0.8346,0.3566,0.2398,0.1405,0.8180,0.6663,0.2000,0.2500,0.1667,0.3333,0.4167,0.2778,4.5668,3.4296 -0,struct_mrx_10_000067,0.8621,0.3466,0.2186,0.1257,0.8365,0.6358,0.2069,0.3000,0.1579,0.4138,0.6000,0.3158,8.0850,1.8720 -0,struct_mrx_10_000068,0.8410,0.3786,0.2873,0.1732,0.8416,0.6343,0.2308,0.2500,0.2143,0.6923,0.7500,0.6429,1.5585,1.4296 -0,struct_mrx_10_000069,0.8495,0.3524,0.2570,0.1514,0.8498,0.6176,0.3571,0.4167,0.3125,0.5000,0.5833,0.4375,3.7497,1.3746 -0,struct_mrx_10_000070,0.6372,0.3309,0.2648,0.1705,0.5922,0.6918,0.1714,0.1500,0.2000,0.5714,0.5000,0.6667,1.5557,3.1860 -0,struct_mrx_10_000071,0.8538,0.2791,0.1890,0.1063,0.8488,0.5917,0.2000,0.3000,0.1500,0.4667,0.7000,0.3500,10.3310,2.1599 -0,struct_mrx_10_000072,0.8982,0.3711,0.2966,0.1774,0.9042,0.5202,0.3810,0.5000,0.3077,0.4762,0.6250,0.3846,5.2091,0.6348 -0,struct_mrx_10_000073,0.7791,0.3662,0.2468,0.1479,0.7446,0.7397,0.1714,0.2143,0.1429,0.2857,0.3571,0.2381,5.4114,4.0151 -0,struct_mrx_10_000074,0.8360,0.3382,0.2822,0.1699,0.8326,0.5516,0.2727,0.3000,0.2500,0.4546,0.5000,0.4167,5.6159,2.1355 -0,struct_mrx_10_000075,0.8426,0.3561,0.2379,0.1391,0.8208,0.6901,0.1765,0.2308,0.1429,0.4706,0.6154,0.3810,6.1055,1.5624 -0,struct_mrx_10_000076,0.8257,0.4338,0.2968,0.1826,0.7929,0.7038,0.2222,0.2727,0.1875,0.3704,0.4546,0.3125,6.0783,3.3652 -0,struct_mrx_10_000077,0.8150,0.3679,0.2648,0.1594,0.7812,0.6760,0.2143,0.2727,0.1765,0.2857,0.3636,0.2353,8.6359,5.2295 -0,struct_mrx_10_000078,0.8430,0.3825,0.2536,0.1502,0.8157,0.5844,0.2069,0.2308,0.1875,0.5517,0.6154,0.5000,3.9174,2.9473 -0,struct_mrx_10_000079,0.9089,0.4172,0.2891,0.1719,0.9077,0.6808,0.2963,0.4000,0.2353,0.5185,0.7000,0.4118,4.7510,1.3842 -0,struct_mrx_10_000080,0.8400,0.3604,0.2268,0.1316,0.8212,0.7102,0.1622,0.2143,0.1304,0.3784,0.5000,0.3044,6.6134,1.3652 -0,struct_mrx_10_000081,0.7721,0.3283,0.2099,0.1220,0.7491,0.6844,0.2105,0.2353,0.1905,0.4737,0.5294,0.4286,3.0145,1.7439 -0,struct_mrx_10_000082,0.8280,0.5282,0.3970,0.2645,0.7947,0.8165,0.2500,0.3077,0.2105,0.5000,0.6154,0.4210,2.9104,2.0669 -0,struct_mrx_10_000083,0.8195,0.4133,0.3337,0.2141,0.7555,0.6594,0.3636,0.4444,0.3077,0.5454,0.6667,0.4615,5.5522,1.5134 -0,struct_mrx_10_000084,0.8360,0.3504,0.2619,0.1549,0.8469,0.6886,0.2000,0.2727,0.1579,0.4000,0.5454,0.3158,6.1132,2.0267 -0,struct_mrx_10_000085,0.6654,0.3298,0.2404,0.1516,0.5810,0.7086,0.1500,0.1304,0.1765,0.5000,0.4348,0.5882,1.7818,3.2370 -0,struct_mrx_10_000086,0.8056,0.3561,0.2539,0.1516,0.7814,0.6523,0.2143,0.2727,0.1765,0.3571,0.4546,0.2941,8.6176,4.3786 -0,struct_mrx_10_000087,0.8062,0.4365,0.2837,0.1741,0.7666,0.8181,0.1364,0.1667,0.1154,0.5000,0.6111,0.4231,3.9017,1.8702 -0,struct_mrx_10_000088,0.9377,0.2158,0.1557,0.0848,0.9471,0.3545,0.3704,0.6250,0.2632,0.5185,0.8750,0.3684,11.5920,0.2435 -0,struct_mrx_10_000089,0.8904,0.3936,0.2518,0.1474,0.8628,0.7071,0.2000,0.3333,0.1429,0.3333,0.5556,0.2381,7.4047,1.1968 -0,struct_mrx_10_000090,0.8576,0.3731,0.2502,0.1468,0.8478,0.6493,0.2222,0.3000,0.1765,0.3704,0.5000,0.2941,6.7857,1.8857 -0,struct_mrx_10_000091,0.8911,0.2417,0.2490,0.1441,0.9164,0.3557,0.3158,0.4286,0.2500,0.5263,0.7143,0.4167,4.9331,0.5067 -0,struct_mrx_10_000092,0.8874,0.4231,0.2652,0.1564,0.8726,0.7741,0.2632,0.3571,0.2083,0.5790,0.7857,0.4583,2.7101,1.0631 -0,struct_mrx_10_000093,0.8316,0.3340,0.2025,0.1156,0.8153,0.6662,0.3158,0.3750,0.2727,0.6316,0.7500,0.5454,1.8608,1.2542 -0,struct_mrx_10_000094,0.8284,0.3010,0.1989,0.1130,0.8266,0.5784,0.1765,0.2727,0.1304,0.3529,0.5454,0.2609,5.9161,1.7555 -0,struct_mrx_10_000095,0.7844,0.4662,0.3091,0.1985,0.6983,0.7489,0.2500,0.2667,0.2353,0.3750,0.4000,0.3529,3.4377,4.6284 -0,struct_mrx_10_000096,0.8697,0.3957,0.2295,0.1330,0.8376,0.7366,0.2162,0.3077,0.1667,0.3784,0.5385,0.2917,4.5502,2.0083 -0,struct_mrx_10_000097,0.8635,0.3814,0.2039,0.1165,0.8169,0.6392,0.2353,0.3333,0.1818,0.4706,0.6667,0.3636,7.0068,1.6033 -0,struct_mrx_10_000098,0.8352,0.2320,0.1478,0.0809,0.8478,0.4884,0.1818,0.2727,0.1364,0.3636,0.5454,0.2727,4.8995,2.4306 -0,struct_mrx_10_000099,0.8650,0.3377,0.2018,0.1142,0.8625,0.6043,0.1875,0.2727,0.1429,0.3750,0.5454,0.2857,6.2804,1.7267
--- a/MIREX_data/2011/2010data/GP6-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8850,0.6887,0.5624,0.4183,0.8582,0.8873,0.2069,0.2000,0.2143,0.6207,0.6000,0.6429,1.1379,0.9022 -0,struct_mrx_10_000001,0.9178,0.5684,0.3771,0.2394,0.8866,0.8548,0.2632,0.3333,0.2174,0.5263,0.6667,0.4348,2.7619,0.9098 -0,struct_mrx_10_000002,0.8588,0.5312,0.3398,0.2149,0.8113,0.8170,0.0952,0.0909,0.1000,0.5238,0.5000,0.5500,0.7176,0.7176 -0,struct_mrx_10_000003,0.8668,0.6343,0.4997,0.3574,0.8303,0.8970,0.1081,0.1176,0.1000,0.6486,0.7059,0.6000,1.4824,1.3845 -0,struct_mrx_10_000004,0.9284,0.6026,0.3862,0.2458,0.9005,0.8503,0.3590,0.4118,0.3182,0.7692,0.8824,0.6818,0.8411,0.6307 -0,struct_mrx_10_000005,0.7664,0.5609,0.3615,0.2532,0.6321,0.8686,0.1538,0.1250,0.2000,0.4231,0.3438,0.5500,1.7089,3.0937 -0,struct_mrx_10_000006,0.7524,0.5425,0.3433,0.2391,0.6089,0.8345,0.1778,0.1482,0.2222,0.5333,0.4444,0.6667,1.7862,4.0827 -0,struct_mrx_10_000007,0.8566,0.5938,0.3487,0.2274,0.7481,0.8352,0.1026,0.1053,0.1000,0.4103,0.4210,0.4000,2.8820,3.5586 -0,struct_mrx_10_000008,0.8314,0.5886,0.3765,0.2558,0.7131,0.8610,0.1778,0.1667,0.1905,0.6222,0.5833,0.6667,1.1558,1.0115 -0,struct_mrx_10_000009,0.8356,0.5212,0.3031,0.1919,0.7208,0.8257,0.1538,0.1765,0.1364,0.4615,0.5294,0.4091,3.9394,1.8766 -0,struct_mrx_10_000010,0.8263,0.5095,0.2905,0.1808,0.7389,0.7725,0.1081,0.1053,0.1111,0.4865,0.4737,0.5000,2.2854,2.3016 -0,struct_mrx_10_000011,0.8034,0.6174,0.4670,0.3447,0.7242,0.8669,0.1622,0.1364,0.2000,0.6486,0.5454,0.8000,0.8464,1.6757 -0,struct_mrx_10_000012,0.8702,0.5528,0.3545,0.2266,0.8141,0.8346,0.1500,0.1500,0.1500,0.6000,0.6000,0.6000,1.2173,1.0472 -0,struct_mrx_10_000013,0.8569,0.5715,0.3483,0.2245,0.7772,0.8282,0.3478,0.3478,0.3478,0.6522,0.6522,0.6522,1.1995,1.1995 -0,struct_mrx_10_000014,0.8688,0.6401,0.4724,0.3334,0.8102,0.8915,0.2326,0.2273,0.2381,0.6046,0.5909,0.6190,1.0579,1.0499 -0,struct_mrx_10_000015,0.9046,0.5981,0.4062,0.2658,0.8609,0.8367,0.2632,0.2632,0.2632,0.6316,0.6316,0.6316,0.7871,0.7871 -0,struct_mrx_10_000016,0.8689,0.6220,0.4683,0.3270,0.8246,0.8820,0.2326,0.2083,0.2632,0.6977,0.6250,0.7895,0.7446,0.7913 -0,struct_mrx_10_000017,0.8216,0.6029,0.4356,0.3093,0.7358,0.8691,0.2439,0.2083,0.2941,0.4390,0.3750,0.5294,0.9578,2.1218 -0,struct_mrx_10_000018,0.9214,0.6259,0.4360,0.2863,0.9131,0.8698,0.2927,0.3000,0.2857,0.7317,0.7500,0.7143,0.7763,0.7622 -0,struct_mrx_10_000019,0.8316,0.4915,0.2746,0.1680,0.7518,0.8224,0.1132,0.1034,0.1250,0.3774,0.3448,0.4167,3.7986,2.9899 -0,struct_mrx_10_000020,0.7890,0.5707,0.3971,0.2788,0.6900,0.8544,0.2667,0.2400,0.3000,0.4889,0.4400,0.5500,1.5888,2.2911 -0,struct_mrx_10_000021,0.6980,0.5329,0.3550,0.2621,0.5499,0.8346,0.0851,0.0645,0.1250,0.3404,0.2581,0.5000,1.5450,3.8170 -0,struct_mrx_10_000022,0.7042,0.5432,0.3813,0.2830,0.5844,0.8186,0.1714,0.1429,0.2143,0.4571,0.3810,0.5714,1.1481,5.0526 -0,struct_mrx_10_000023,0.7511,0.5970,0.4217,0.3220,0.6108,0.8440,0.1538,0.1304,0.1875,0.4103,0.3478,0.5000,1.5310,4.0701 -0,struct_mrx_10_000024,0.7239,0.5778,0.4066,0.3090,0.5943,0.8384,0.2000,0.1538,0.2857,0.5500,0.4231,0.7857,0.7603,2.1303 -0,struct_mrx_10_000025,0.8528,0.4789,0.2716,0.1637,0.7963,0.7817,0.3044,0.3044,0.3044,0.5652,0.5652,0.5652,1.0483,0.6181 -0,struct_mrx_10_000026,0.8873,0.6185,0.4003,0.2642,0.8256,0.8657,0.2439,0.2632,0.2273,0.6829,0.7368,0.6364,0.9396,0.8662 -0,struct_mrx_10_000027,0.8154,0.5240,0.3186,0.2052,0.7124,0.8474,0.1333,0.1304,0.1364,0.3111,0.3044,0.3182,3.4139,3.9199 -0,struct_mrx_10_000028,0.8486,0.5865,0.3726,0.2460,0.7681,0.8633,0.2308,0.2069,0.2609,0.5000,0.4483,0.5652,0.9193,0.9614 -0,struct_mrx_10_000029,0.8176,0.5649,0.3695,0.2495,0.7120,0.8032,0.1667,0.1500,0.1875,0.4444,0.4000,0.5000,1.1041,1.1042 -0,struct_mrx_10_000030,0.8272,0.4602,0.2510,0.1503,0.7611,0.7218,0.1081,0.1111,0.1053,0.4865,0.5000,0.4737,2.2467,2.0951 -0,struct_mrx_10_000031,0.7776,0.5839,0.4227,0.3076,0.6751,0.8754,0.2667,0.2222,0.3333,0.5778,0.4815,0.7222,0.7686,1.3797 -0,struct_mrx_10_000032,0.8805,0.6511,0.4894,0.3499,0.8142,0.8978,0.4000,0.3913,0.4091,0.6667,0.6522,0.6818,0.5139,0.4863 -0,struct_mrx_10_000033,0.9002,0.8488,0.7779,0.7132,0.8555,0.9540,0.3750,0.3333,0.4286,0.5625,0.5000,0.6429,0.5437,0.7614 -0,struct_mrx_10_000034,0.8132,0.3806,0.2253,0.1330,0.7376,0.7070,0.2000,0.2000,0.2000,0.4000,0.4000,0.4000,2.8645,2.8645 -0,struct_mrx_10_000035,0.8638,0.5779,0.4183,0.2798,0.8284,0.8243,0.2500,0.2273,0.2778,0.5000,0.4546,0.5556,1.1076,0.7543 -0,struct_mrx_10_000036,0.8341,0.6686,0.5093,0.3807,0.7690,0.8691,0.1765,0.1579,0.2000,0.5294,0.4737,0.6000,1.7343,2.4234 -0,struct_mrx_10_000037,0.8115,0.5304,0.3302,0.2138,0.7245,0.7879,0.1026,0.0952,0.1111,0.5641,0.5238,0.6111,1.4786,1.4934 -0,struct_mrx_10_000038,0.7641,0.5620,0.4007,0.2882,0.6575,0.8532,0.1143,0.1053,0.1250,0.4000,0.3684,0.4375,2.6119,3.3052 -0,struct_mrx_10_000039,0.8744,0.6703,0.5189,0.3803,0.8162,0.8980,0.2051,0.2000,0.2105,0.7692,0.7500,0.7895,0.9012,0.9316 -0,struct_mrx_10_000040,0.8561,0.6153,0.4632,0.3267,0.7958,0.8866,0.2105,0.2222,0.2000,0.6316,0.6667,0.6000,1.2661,1.2519 -0,struct_mrx_10_000041,0.8511,0.6094,0.4292,0.2962,0.7792,0.8821,0.1053,0.1176,0.0952,0.4737,0.5294,0.4286,2.9247,2.9247 -0,struct_mrx_10_000042,0.8474,0.6766,0.4885,0.3573,0.7721,0.8569,0.0526,0.0476,0.0588,0.6316,0.5714,0.7059,1.8000,1.8296 -0,struct_mrx_10_000043,0.8778,0.5610,0.3358,0.2118,0.8100,0.7918,0.1714,0.1875,0.1579,0.5714,0.6250,0.5263,1.3782,0.8776 -0,struct_mrx_10_000044,0.8500,0.6283,0.4720,0.3335,0.8074,0.8679,0.1429,0.1250,0.1667,0.7143,0.6250,0.8333,1.0597,1.2104 -0,struct_mrx_10_000045,0.8112,0.4527,0.2567,0.1561,0.7208,0.8087,0.1200,0.1200,0.1200,0.4800,0.4800,0.4800,2.7995,2.4583 -0,struct_mrx_10_000046,0.8679,0.6345,0.4871,0.3479,0.8122,0.8946,0.2500,0.2500,0.2500,0.6500,0.6500,0.6500,0.8891,0.6996 -0,struct_mrx_10_000047,0.8382,0.5482,0.3552,0.2305,0.7745,0.8290,0.2553,0.2222,0.3000,0.5957,0.5185,0.7000,1.0240,1.1292 -0,struct_mrx_10_000048,0.9037,0.6907,0.4854,0.3389,0.8550,0.8772,0.3000,0.2857,0.3158,0.5000,0.4762,0.5263,0.6900,0.6111 -0,struct_mrx_10_000049,0.6645,0.5033,0.3545,0.2633,0.5424,0.8298,0.0870,0.0645,0.1333,0.3044,0.2258,0.4667,2.0301,3.7839 -0,struct_mrx_10_000050,0.8173,0.5025,0.3037,0.1916,0.7312,0.8320,0.1887,0.1667,0.2174,0.5660,0.5000,0.6522,0.8173,0.9030 -0,struct_mrx_10_000051,0.6416,0.5779,0.3693,0.3111,0.4543,0.8370,0.0870,0.0606,0.1538,0.2609,0.1818,0.4615,1.9708,4.7401 -0,struct_mrx_10_000052,0.7371,0.5410,0.3879,0.2768,0.6483,0.8459,0.0976,0.0833,0.1176,0.4390,0.3750,0.5294,2.1398,3.1743 -0,struct_mrx_10_000053,0.8002,0.5003,0.2874,0.1817,0.6868,0.8479,0.1111,0.1034,0.1200,0.4074,0.3793,0.4400,3.2011,3.2907 -0,struct_mrx_10_000054,0.8016,0.6396,0.4505,0.3262,0.7282,0.8458,0.3590,0.2917,0.4667,0.5641,0.4583,0.7333,0.4933,0.8207 -0,struct_mrx_10_000055,0.8919,0.8176,0.7242,0.6387,0.8360,0.9425,0.3333,0.3125,0.3571,0.7333,0.6875,0.7857,0.5358,0.5358 -0,struct_mrx_10_000056,0.8399,0.6625,0.5181,0.3928,0.7607,0.8912,0.1500,0.1304,0.1765,0.6500,0.5652,0.7647,0.7562,1.9358 -0,struct_mrx_10_000057,0.8829,0.5219,0.2914,0.1765,0.8353,0.7672,0.1860,0.2000,0.1739,0.5581,0.6000,0.5217,1.0927,0.9087 -0,struct_mrx_10_000058,0.8620,0.6082,0.3730,0.2456,0.7748,0.8512,0.2326,0.2500,0.2174,0.6046,0.6500,0.5652,1.8759,1.6610 -0,struct_mrx_10_000059,0.7646,0.5501,0.3464,0.2386,0.6322,0.8409,0.0870,0.0769,0.1000,0.6087,0.5385,0.7000,1.2605,1.9830 -0,struct_mrx_10_000060,0.7893,0.5882,0.3865,0.2712,0.6726,0.8560,0.1277,0.1154,0.1429,0.6383,0.5769,0.7143,1.2194,1.2195 -0,struct_mrx_10_000061,0.7970,0.7397,0.6020,0.5347,0.6888,0.9150,0.0690,0.0667,0.0714,0.8276,0.8000,0.8571,1.0190,1.4263 -0,struct_mrx_10_000062,0.8612,0.7183,0.5760,0.4560,0.7815,0.8974,0.4324,0.3636,0.5333,0.6486,0.5454,0.8000,0.4514,0.4514 -0,struct_mrx_10_000063,0.7562,0.4946,0.2712,0.1759,0.5919,0.8404,0.1786,0.1515,0.2174,0.4286,0.3636,0.5217,1.7281,2.9230 -0,struct_mrx_10_000064,0.6910,0.5974,0.4401,0.3636,0.5574,0.8463,0.2222,0.1739,0.3077,0.5556,0.4348,0.7692,0.9328,4.5565 -0,struct_mrx_10_000065,0.6829,0.5976,0.4259,0.3505,0.5426,0.8612,0.1395,0.1034,0.2143,0.3721,0.2759,0.5714,0.8439,4.2510 -0,struct_mrx_10_000066,0.7726,0.5320,0.3388,0.2298,0.6442,0.8386,0.0513,0.0476,0.0556,0.4103,0.3810,0.4444,2.6048,4.2124 -0,struct_mrx_10_000067,0.8358,0.5366,0.3738,0.2486,0.7529,0.8457,0.2051,0.2000,0.2105,0.4103,0.4000,0.4210,1.7620,1.2063 -0,struct_mrx_10_000068,0.7653,0.5922,0.4063,0.2946,0.6545,0.8316,0.1176,0.1000,0.1429,0.5294,0.4500,0.6429,1.8182,2.5565 -0,struct_mrx_10_000069,0.6969,0.5746,0.3923,0.3114,0.5299,0.8710,0.1667,0.1250,0.2500,0.5000,0.3750,0.7500,0.9689,2.1307 -0,struct_mrx_10_000070,0.6299,0.5134,0.3402,0.2671,0.4684,0.8301,0.2727,0.2069,0.4000,0.5000,0.3793,0.7333,0.5521,3.6155 -0,struct_mrx_10_000071,0.8495,0.6146,0.4451,0.3136,0.7669,0.8937,0.0930,0.0870,0.1000,0.6046,0.5652,0.6500,1.3308,1.4082 -0,struct_mrx_10_000072,0.7310,0.5976,0.4357,0.3382,0.6123,0.8227,0.1538,0.1154,0.2308,0.4615,0.3462,0.6923,0.8631,4.6839 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-0,struct_mrx_10_000097,0.8714,0.5949,0.3560,0.2302,0.7848,0.8402,0.2791,0.2857,0.2727,0.5116,0.5238,0.5000,1.6776,1.2098 -0,struct_mrx_10_000098,0.8516,0.5318,0.3266,0.2054,0.7968,0.8284,0.1304,0.1250,0.1364,0.5652,0.5417,0.5909,1.8507,1.3163 -0,struct_mrx_10_000099,0.7779,0.5387,0.3538,0.2409,0.6660,0.8582,0.2449,0.2143,0.2857,0.5714,0.5000,0.6667,1.3145,1.8176
--- a/MIREX_data/2011/2010data/MHRAF1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8850,0.4074,0.3146,0.1916,0.8784,0.6804,0.3200,0.3636,0.2857,0.4800,0.5454,0.4286,1.8140,0.6046 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-0,struct_mrx_10_000017,0.8893,0.5186,0.4020,0.2593,0.8937,0.8161,0.1936,0.2143,0.1765,0.8387,0.9286,0.7647,1.1457,1.0238 -0,struct_mrx_10_000018,0.9067,0.3931,0.2532,0.1473,0.9009,0.7097,0.1936,0.3000,0.1429,0.5806,0.9000,0.4286,2.6715,1.3447 -0,struct_mrx_10_000019,0.9084,0.4347,0.2604,0.1525,0.8900,0.7728,0.3243,0.4615,0.2500,0.4324,0.6154,0.3333,6.0997,0.9198 -0,struct_mrx_10_000020,0.9047,0.2715,0.2069,0.1166,0.9149,0.5113,0.2581,0.3636,0.2000,0.4516,0.6364,0.3500,3.0739,0.8380 -0,struct_mrx_10_000021,0.7824,0.2703,0.2064,0.1194,0.7606,0.5164,0.1667,0.2500,0.1250,0.2500,0.3750,0.1875,7.4145,3.5462 -0,struct_mrx_10_000022,0.8678,0.5108,0.4138,0.2713,0.8718,0.7629,0.0800,0.0909,0.0714,0.3200,0.3636,0.2857,3.4736,3.2593 -0,struct_mrx_10_000023,0.9474,0.6066,0.5116,0.3496,0.9534,0.8295,0.6000,0.6429,0.5625,0.8000,0.8571,0.7500,0.3356,0.3206 -0,struct_mrx_10_000024,0.9163,0.5612,0.4479,0.2953,0.9272,0.7855,0.3846,0.4167,0.3571,0.8462,0.9167,0.7857,0.5860,0.5860 -0,struct_mrx_10_000025,0.9186,0.4454,0.2963,0.1767,0.9165,0.7766,0.2941,0.4546,0.2174,0.5294,0.8182,0.3913,3.3729,0.6862 -0,struct_mrx_10_000026,0.9443,0.4492,0.2636,0.1530,0.9506,0.7083,0.1875,0.3000,0.1364,0.6250,1.0000,0.4546,2.0975,0.6286 -0,struct_mrx_10_000027,0.9456,0.3094,0.1975,0.1103,0.9471,0.6170,0.0690,0.1429,0.0455,0.2069,0.4286,0.1364,11.9690,4.4782 -0,struct_mrx_10_000028,0.9710,0.4415,0.2700,0.1568,0.9687,0.7242,0.2581,0.5000,0.1739,0.3871,0.7500,0.2609,7.1781,0.5135 -0,struct_mrx_10_000029,0.8585,0.3751,0.2596,0.1536,0.8386,0.6151,0.1429,0.1667,0.1250,0.4286,0.5000,0.3750,4.3528,2.8393 -0,struct_mrx_10_000030,0.9005,0.3663,0.2447,0.1419,0.8877,0.6619,0.3571,0.5556,0.2632,0.3571,0.5556,0.2632,3.9128,0.4813 -0,struct_mrx_10_000031,0.9438,0.5087,0.3490,0.2140,0.9450,0.7614,0.2667,0.3333,0.2222,0.8000,1.0000,0.6667,1.4313,1.1883 -0,struct_mrx_10_000032,0.7958,0.3069,0.1890,0.1078,0.7657,0.6030,0.1579,0.1875,0.1364,0.3684,0.4375,0.3182,4.0795,4.0795 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-0,struct_mrx_10_000041,0.9779,0.3186,0.1875,0.1037,0.9797,0.5167,0.5333,0.8889,0.3810,0.6000,1.0000,0.4286,1.5965,0.1878 -0,struct_mrx_10_000042,0.9733,0.5016,0.3830,0.2373,0.9923,0.7133,0.6154,0.8889,0.4706,0.6923,1.0000,0.5294,0.4572,0.3545 -0,struct_mrx_10_000043,0.9210,0.3743,0.2314,0.1326,0.9052,0.6072,0.2857,0.4444,0.2105,0.5000,0.7778,0.3684,6.9671,0.5346 -0,struct_mrx_10_000044,0.9064,0.1498,0.1522,0.0829,0.9266,0.2419,0.1600,0.2857,0.1111,0.2400,0.4286,0.1667,4.9665,4.6543 -0,struct_mrx_10_000045,0.9224,0.3662,0.2040,0.1149,0.9074,0.6739,0.1667,0.2727,0.1200,0.3889,0.6364,0.2800,5.7860,0.9648 -0,struct_mrx_10_000046,0.9172,0.3897,0.2804,0.1651,0.9301,0.7040,0.3448,0.5556,0.2500,0.4138,0.6667,0.3000,3.1474,0.3176 -0,struct_mrx_10_000047,0.9359,0.4963,0.3727,0.2316,0.9539,0.8050,0.2857,0.3333,0.2500,0.7429,0.8667,0.6500,0.8261,0.8224 -0,struct_mrx_10_000048,0.9105,0.5506,0.4172,0.2712,0.9041,0.8293,0.5000,0.6154,0.4210,0.6875,0.8462,0.5790,0.6866,0.3444 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-0,struct_mrx_10_000097,0.8594,0.3152,0.1783,0.0997,0.8416,0.5615,0.1143,0.1538,0.0909,0.5143,0.6923,0.4091,3.0005,1.4260 -0,struct_mrx_10_000098,0.8922,0.4941,0.3198,0.1962,0.8632,0.8083,0.2778,0.3571,0.2273,0.7222,0.9286,0.5909,2.1218,0.7071 -0,struct_mrx_10_000099,0.9172,0.2590,0.1873,0.1040,0.9366,0.5260,0.3571,0.7143,0.2381,0.3571,0.7143,0.2381,9.0758,0.4373
--- a/MIREX_data/2011/2010data/MHRAF2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8887,0.3545,0.2924,0.1745,0.9020,0.6320,0.3333,0.4000,0.2857,0.5000,0.6000,0.4286,2.5130,1.6736 -0,struct_mrx_10_000001,0.9736,0.3422,0.1782,0.0981,0.9732,0.5537,0.2581,0.5000,0.1739,0.3871,0.7500,0.2609,9.1636,0.4015 -0,struct_mrx_10_000002,0.8942,0.4551,0.2995,0.1802,0.8858,0.7595,0.2778,0.3125,0.2500,0.6667,0.7500,0.6000,1.8786,1.4430 -0,struct_mrx_10_000003,0.9162,0.3605,0.2250,0.1283,0.9121,0.6097,0.3226,0.4546,0.2500,0.7097,1.0000,0.5500,2.4381,0.6610 -0,struct_mrx_10_000004,0.9099,0.3634,0.1874,0.1048,0.8854,0.5998,0.1765,0.2500,0.1364,0.3529,0.5000,0.2727,5.5172,3.0127 -0,struct_mrx_10_000005,0.8653,0.3459,0.2464,0.1434,0.8733,0.6862,0.1212,0.1538,0.1000,0.4848,0.6154,0.4000,3.0800,1.5606 -0,struct_mrx_10_000006,0.9443,0.4459,0.3292,0.1985,0.9645,0.7164,0.4667,0.5833,0.3889,0.8000,1.0000,0.6667,0.5296,0.4203 -0,struct_mrx_10_000007,0.9419,0.3537,0.2314,0.1319,0.9397,0.6360,0.2222,0.4286,0.1500,0.2222,0.4286,0.1500,12.3240,3.8774 -0,struct_mrx_10_000008,0.8793,0.3436,0.1878,0.1058,0.8337,0.5735,0.1290,0.2000,0.0952,0.3871,0.6000,0.2857,5.0843,2.1359 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-0,struct_mrx_10_000017,0.8999,0.5257,0.4049,0.2610,0.9017,0.8173,0.3226,0.3571,0.2941,0.8387,0.9286,0.7647,1.1457,0.9988 -0,struct_mrx_10_000018,0.9533,0.4980,0.3251,0.1961,0.9493,0.7867,0.5882,0.7692,0.4762,0.7647,1.0000,0.6190,0.4522,0.3832 -0,struct_mrx_10_000019,0.9207,0.4694,0.3003,0.1796,0.9149,0.8087,0.2703,0.3846,0.2083,0.5405,0.7692,0.4167,4.0551,1.4856 -0,struct_mrx_10_000020,0.9555,0.4218,0.3091,0.1842,0.9612,0.7021,0.4516,0.6364,0.3500,0.7097,1.0000,0.5500,1.4452,0.2720 -0,struct_mrx_10_000021,0.8142,0.3025,0.2314,0.1352,0.8016,0.5566,0.2500,0.3750,0.1875,0.4167,0.6250,0.3125,7.4145,2.3645 -0,struct_mrx_10_000022,0.7589,0.4714,0.3437,0.2281,0.6963,0.7450,0.0741,0.0769,0.0714,0.2963,0.3077,0.2857,3.9215,4.1551 -0,struct_mrx_10_000023,0.9106,0.5809,0.4856,0.3309,0.9117,0.8191,0.4667,0.5000,0.4375,0.8000,0.8571,0.7500,1.2011,0.5386 -0,struct_mrx_10_000024,0.9420,0.5825,0.4667,0.3084,0.9586,0.7943,0.3846,0.4167,0.3571,0.9231,1.0000,0.8571,0.5860,0.5860 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-0,struct_mrx_10_000073,0.8901,0.4089,0.2562,0.1496,0.8933,0.7005,0.2222,0.2667,0.1905,0.7222,0.8667,0.6190,1.3716,0.7541 -0,struct_mrx_10_000074,0.8340,0.5983,0.5301,0.3903,0.8258,0.8475,0.1667,0.1667,0.1667,0.4167,0.4167,0.4167,3.0062,3.0538 -0,struct_mrx_10_000075,0.9070,0.3690,0.2378,0.1371,0.8962,0.6608,0.1143,0.1429,0.0952,0.5714,0.7143,0.4762,2.5697,2.3778 -0,struct_mrx_10_000076,0.8949,0.4928,0.3805,0.2428,0.8795,0.7731,0.2963,0.3636,0.2500,0.5185,0.6364,0.4375,4.1212,1.3320 -0,struct_mrx_10_000077,0.8656,0.5818,0.4566,0.3152,0.8278,0.8526,0.2069,0.2500,0.1765,0.3448,0.4167,0.2941,4.9558,4.0422 -0,struct_mrx_10_000078,0.7381,0.4818,0.3548,0.2432,0.6549,0.7940,0.1379,0.1538,0.1250,0.3448,0.3846,0.3125,6.2152,6.0723 -0,struct_mrx_10_000079,0.9317,0.4806,0.3279,0.1994,0.9228,0.7328,0.2000,0.2308,0.1765,0.7333,0.8462,0.6471,2.6786,2.5510 -0,struct_mrx_10_000080,0.8717,0.4987,0.3239,0.2014,0.8273,0.8226,0.0976,0.1111,0.0870,0.6342,0.7222,0.5652,2.0587,2.0469 -0,struct_mrx_10_000081,0.8439,0.3816,0.2111,0.1215,0.8045,0.6612,0.1818,0.2500,0.1429,0.3030,0.4167,0.2381,4.6213,3.8956 -0,struct_mrx_10_000082,0.8844,0.4957,0.3671,0.2324,0.8737,0.7686,0.1379,0.2000,0.1053,0.4828,0.7000,0.3684,2.0840,1.0453 -0,struct_mrx_10_000083,0.8334,0.6074,0.4626,0.3384,0.7310,0.8098,0.4615,0.4615,0.4615,0.7692,0.7692,0.7692,0.4858,0.5478 -0,struct_mrx_10_000084,0.8342,0.4544,0.3022,0.1872,0.7832,0.7641,0.3529,0.4000,0.3158,0.6471,0.7333,0.5790,1.6168,1.2742 -0,struct_mrx_10_000085,0.8536,0.5032,0.3792,0.2470,0.8167,0.7874,0.4000,0.4615,0.3529,0.6000,0.6923,0.5294,1.0430,1.0430 -0,struct_mrx_10_000086,0.8042,0.3992,0.2841,0.1736,0.7809,0.7014,0.1936,0.2143,0.1765,0.3871,0.4286,0.3529,4.6614,5.0509 -0,struct_mrx_10_000087,0.9297,0.4663,0.2968,0.1764,0.9357,0.7921,0.0526,0.0833,0.0385,0.6316,1.0000,0.4615,2.6845,0.8068 -0,struct_mrx_10_000088,0.8345,0.5066,0.3554,0.2300,0.7817,0.8209,0.1818,0.2143,0.1579,0.4848,0.5714,0.4210,3.6316,2.3530 -0,struct_mrx_10_000089,0.8811,0.4577,0.3014,0.1834,0.8459,0.7760,0.2500,0.3636,0.1905,0.3125,0.4546,0.2381,5.5228,3.3255 -0,struct_mrx_10_000090,0.8995,0.4399,0.3156,0.1914,0.8990,0.7299,0.0667,0.0769,0.0588,0.8000,0.9231,0.7059,1.9585,1.9502 -0,struct_mrx_10_000091,0.8274,0.5257,0.4426,0.3054,0.8038,0.7642,0.4286,0.3750,0.5000,0.7857,0.6875,0.9167,0.3753,0.6777 -0,struct_mrx_10_000092,0.8936,0.3796,0.2055,0.1164,0.8733,0.6837,0.1053,0.1429,0.0833,0.5790,0.7857,0.4583,1.6442,1.3966 -0,struct_mrx_10_000093,0.8957,0.4950,0.3025,0.1836,0.8579,0.7932,0.2941,0.4167,0.2273,0.6471,0.9167,0.5000,2.8794,1.2868 -0,struct_mrx_10_000094,0.9379,0.4389,0.2573,0.1499,0.9079,0.6715,0.3030,0.5000,0.2174,0.5454,0.9000,0.3913,1.4171,0.4178 -0,struct_mrx_10_000095,0.9900,0.2653,0.2082,0.1163,0.9957,0.3924,0.3333,0.5714,0.2353,0.5833,1.0000,0.4118,1.9038,0.3714 -0,struct_mrx_10_000096,0.9528,0.3354,0.1877,0.1040,0.9619,0.6070,0.2424,0.4444,0.1667,0.5454,1.0000,0.3750,6.8912,0.6093 -0,struct_mrx_10_000097,0.9481,0.4182,0.2656,0.1542,0.9560,0.7023,0.4000,0.5385,0.3182,0.6286,0.8462,0.5000,1.0433,0.3784 -0,struct_mrx_10_000098,0.8953,0.4973,0.3219,0.1977,0.8664,0.8095,0.3333,0.4286,0.2727,0.7222,0.9286,0.5909,2.1218,0.7071 -0,struct_mrx_10_000099,0.9249,0.2384,0.1711,0.0940,0.9515,0.4625,0.2143,0.4286,0.1429,0.3571,0.7143,0.2381,9.0758,1.8566
--- a/MIREX_data/2011/2010data/MHRAF3-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.7991,0.3882,0.3202,0.1996,0.8079,0.7113,0.1667,0.2000,0.1429,0.4167,0.5000,0.3571,3.6515,3.0374 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-0,struct_mrx_10_000041,0.9892,0.3157,0.1864,0.1029,0.9879,0.5103,0.4667,0.7778,0.3333,0.6000,1.0000,0.4286,1.7009,0.0477 -0,struct_mrx_10_000042,0.9543,0.4809,0.3740,0.2313,0.9768,0.7085,0.4000,0.6250,0.2941,0.6400,1.0000,0.4706,1.3585,0.4356 -0,struct_mrx_10_000043,0.9244,0.4564,0.3028,0.1814,0.9168,0.7248,0.2857,0.4444,0.2105,0.5714,0.8889,0.4210,3.4719,1.2688 -0,struct_mrx_10_000044,0.8248,0.3452,0.2274,0.1325,0.8024,0.6001,0.2222,0.3333,0.1667,0.4444,0.6667,0.3333,5.2055,1.0802 -0,struct_mrx_10_000045,0.9274,0.4805,0.3096,0.1860,0.9230,0.8127,0.1765,0.3333,0.1200,0.3529,0.6667,0.2400,3.9448,1.0826 -0,struct_mrx_10_000046,0.9520,0.3512,0.2354,0.1340,0.9652,0.6113,0.2143,0.3750,0.1500,0.5714,1.0000,0.4000,4.5484,0.5580 -0,struct_mrx_10_000047,0.9118,0.5732,0.4314,0.2832,0.9045,0.8550,0.2500,0.3333,0.2000,0.5000,0.6667,0.4000,3.4860,2.6201 -0,struct_mrx_10_000048,0.8742,0.4567,0.2996,0.1822,0.8428,0.7327,0.2857,0.4444,0.2105,0.3571,0.5556,0.2632,3.6567,0.5185 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-0,struct_mrx_10_000057,0.9427,0.4566,0.2869,0.1691,0.9466,0.7321,0.2941,0.4546,0.2174,0.5882,0.9091,0.4348,2.1867,0.9026 -0,struct_mrx_10_000058,0.9081,0.4752,0.2870,0.1719,0.8683,0.7568,0.0625,0.1111,0.0435,0.3750,0.6667,0.2609,4.3829,2.2268 -0,struct_mrx_10_000059,0.9498,0.3131,0.2088,0.1170,0.9679,0.5084,0.0769,0.1667,0.0500,0.2308,0.5000,0.1500,8.2563,3.0009 -0,struct_mrx_10_000060,0.9204,0.4864,0.3292,0.2004,0.9198,0.7473,0.2759,0.5000,0.1905,0.2759,0.5000,0.1905,5.2292,1.8717 -0,struct_mrx_10_000061,0.8944,0.3557,0.3032,0.1816,0.9176,0.6056,0.0909,0.1250,0.0714,0.7273,1.0000,0.5714,1.6338,1.4684 -0,struct_mrx_10_000062,0.8144,0.3014,0.2434,0.1422,0.8453,0.5265,0.0833,0.1111,0.0667,0.4167,0.5556,0.3333,3.5390,1.0310 -0,struct_mrx_10_000063,0.9025,0.4877,0.3382,0.2085,0.8948,0.8251,0.3636,0.6000,0.2609,0.3636,0.6000,0.2609,6.8010,0.3846 -0,struct_mrx_10_000064,0.8596,0.4358,0.3126,0.1926,0.8288,0.6050,0.1905,0.2500,0.1538,0.6667,0.8750,0.5385,2.0500,1.8208 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-0,struct_mrx_10_000081,0.9415,0.4090,0.2688,0.1566,0.9499,0.7080,0.2667,0.4444,0.1905,0.6000,1.0000,0.4286,2.3288,1.1000 -0,struct_mrx_10_000082,0.8886,0.4283,0.3426,0.2104,0.9207,0.7287,0.1538,0.2857,0.1053,0.5385,1.0000,0.3684,3.2770,1.1059 -0,struct_mrx_10_000083,0.8118,0.3328,0.2574,0.1556,0.7442,0.5166,0.0909,0.1111,0.0769,0.4546,0.5556,0.3846,2.6564,1.0682 -0,struct_mrx_10_000084,0.8962,0.5337,0.3773,0.2403,0.8783,0.8106,0.1936,0.2500,0.1579,0.6452,0.8333,0.5263,2.2740,1.6900 -0,struct_mrx_10_000085,0.8582,0.5559,0.4157,0.2760,0.8424,0.8106,0.0645,0.0714,0.0588,0.7742,0.8571,0.7059,1.7808,1.8113 -0,struct_mrx_10_000086,0.8629,0.4296,0.2518,0.1491,0.8082,0.6363,0.2143,0.2727,0.1765,0.2857,0.3636,0.2353,5.9884,5.8079 -0,struct_mrx_10_000087,0.9434,0.4620,0.2896,0.1712,0.9393,0.7846,0.2703,0.4546,0.1923,0.5946,1.0000,0.4231,3.1084,0.6238 -0,struct_mrx_10_000088,0.9368,0.4319,0.2984,0.1768,0.9570,0.7193,0.1429,0.2222,0.1053,0.6429,1.0000,0.4737,1.5319,0.6943 -0,struct_mrx_10_000089,0.9276,0.3927,0.2634,0.1533,0.9346,0.7009,0.2759,0.5000,0.1905,0.3448,0.6250,0.2381,3.8523,0.4697 -0,struct_mrx_10_000090,0.8939,0.4548,0.3605,0.2244,0.9151,0.7736,0.2222,0.3000,0.1765,0.6667,0.9000,0.5294,1.5137,0.8242 -0,struct_mrx_10_000091,0.7780,0.3944,0.3620,0.2389,0.7468,0.7260,0.0909,0.1000,0.0833,0.1818,0.2000,0.1667,4.5472,4.5472 -0,struct_mrx_10_000092,0.9708,0.1198,0.1028,0.0543,0.9812,0.1977,0.0667,0.1667,0.0417,0.4000,1.0000,0.2500,9.8904,1.4951 -0,struct_mrx_10_000093,0.8819,0.3508,0.2181,0.1245,0.8818,0.6676,0.1333,0.2500,0.0909,0.2667,0.5000,0.1818,5.4225,3.0846 -0,struct_mrx_10_000094,0.8456,0.4928,0.3389,0.2151,0.7984,0.8046,0.1818,0.3000,0.1304,0.3636,0.6000,0.2609,4.8645,2.5459 -0,struct_mrx_10_000095,0.9761,0.2566,0.2080,0.1161,0.9953,0.3881,0.1739,0.3333,0.1176,0.5217,1.0000,0.3529,1.8236,0.7650 -0,struct_mrx_10_000096,0.9533,0.3532,0.2072,0.1161,0.9631,0.6543,0.1875,0.3750,0.1250,0.5000,1.0000,0.3333,4.2557,0.8528 -0,struct_mrx_10_000097,0.9443,0.2064,0.1381,0.0744,0.9601,0.3285,0.1290,0.2222,0.0909,0.4516,0.7778,0.3182,4.8070,2.1314 -0,struct_mrx_10_000098,0.8700,0.4668,0.2862,0.1724,0.8418,0.7805,0.0571,0.0769,0.0455,0.5714,0.7692,0.4546,2.4359,1.4854 -0,struct_mrx_10_000099,0.9137,0.3268,0.2157,0.1218,0.9436,0.5994,0.2857,0.5714,0.1905,0.4286,0.8571,0.2857,5.2822,0.2320
--- a/MIREX_data/2011/2010data/SBVRS1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,struct_mrx_10_000000,0.8879,0.4524,0.3673,0.2311,0.8953,0.7411,0.6207,0.6000,0.6429,0.7586,0.7333,0.7857,0.0174,0.0232 -0,struct_mrx_10_000001,0.8615,0.7101,0.4390,0.3181,0.7078,0.9100,0.5556,0.4839,0.6522,0.6667,0.5806,0.7826,0.0464,0.5468 -0,struct_mrx_10_000002,0.9315,0.8361,0.7480,0.6354,0.9092,0.9632,0.3415,0.3333,0.3500,0.8780,0.8571,0.9000,1.0449,1.0913 -0,struct_mrx_10_000003,0.8992,0.3222,0.2224,0.1262,0.9336,0.5904,0.0588,0.0714,0.0500,0.5294,0.6429,0.4500,2.7259,2.2930 -0,struct_mrx_10_000004,0.9044,0.3415,0.2277,0.1300,0.9170,0.6730,0.3333,0.3500,0.3182,0.6667,0.7000,0.6364,1.8460,1.8460 -0,struct_mrx_10_000005,0.8572,0.5744,0.3598,0.2342,0.7761,0.8391,0.3158,0.3333,0.3000,0.4210,0.4444,0.4000,2.8793,3.8429 -0,struct_mrx_10_000006,0.8837,0.4602,0.3341,0.2057,0.8882,0.7443,0.3030,0.3333,0.2778,0.7273,0.8000,0.6667,1.6557,1.6485 -0,struct_mrx_10_000007,0.8432,0.7628,0.5663,0.4713,0.7094,0.9363,0.3810,0.3636,0.4000,0.6667,0.6364,0.7000,1.1805,1.6062 -0,struct_mrx_10_000008,0.7929,0.6895,0.4560,0.3721,0.5889,0.9167,0.0952,0.0952,0.0952,0.6190,0.6190,0.6190,2.4149,2.4149 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-0,struct_mrx_10_000017,0.8254,0.3979,0.2799,0.1693,0.8079,0.7141,0.3030,0.3125,0.2941,0.5454,0.5625,0.5294,2.3336,2.3394 -0,struct_mrx_10_000018,0.9394,0.6925,0.5103,0.3566,0.8973,0.9060,0.5000,0.5263,0.4762,0.7500,0.7895,0.7143,0.2206,0.0464 -0,struct_mrx_10_000019,0.9566,0.6579,0.4297,0.2799,0.9242,0.8894,0.7917,0.7917,0.7917,0.8750,0.8750,0.8750,0.0116,0.0116 -0,struct_mrx_10_000020,0.7570,0.5605,0.3618,0.2591,0.5993,0.8519,0.1765,0.2143,0.1500,0.3529,0.4286,0.3000,5.7427,4.4640 -0,struct_mrx_10_000021,0.7305,0.5313,0.3656,0.2600,0.6160,0.8230,0.0667,0.0714,0.0625,0.2000,0.2143,0.1875,3.8742,4.4247 -0,struct_mrx_10_000022,0.6498,0.5644,0.3641,0.2952,0.4752,0.8408,0.0976,0.0741,0.1429,0.1951,0.1482,0.2857,3.2839,9.4897 -0,struct_mrx_10_000023,0.7199,0.6074,0.3845,0.2990,0.5384,0.8377,0.3784,0.3333,0.4375,0.4324,0.3810,0.5000,0.6581,3.0817 -0,struct_mrx_10_000024,0.7840,0.3652,0.3037,0.1882,0.7855,0.6618,0.2759,0.2667,0.2857,0.4828,0.4667,0.5000,2.1404,3.9991 -0,struct_mrx_10_000025,0.8995,0.5795,0.3824,0.2462,0.8562,0.8572,0.3111,0.3182,0.3044,0.8000,0.8182,0.7826,1.8495,1.8428 -0,struct_mrx_10_000026,0.8595,0.5531,0.2756,0.1691,0.7447,0.7874,0.4231,0.3667,0.5000,0.7308,0.6333,0.8636,0.2084,1.3329 -0,struct_mrx_10_000027,0.9254,0.6137,0.3723,0.2359,0.8823,0.8532,0.6046,0.6190,0.5909,0.7907,0.8095,0.7727,0.0359,0.0273 -0,struct_mrx_10_000028,0.8880,0.5439,0.3428,0.2158,0.8328,0.8317,0.3556,0.3636,0.3478,0.6667,0.6818,0.6522,1.0479,1.8818 -0,struct_mrx_10_000029,0.8580,0.6056,0.4383,0.3050,0.7788,0.8400,0.4667,0.5000,0.4375,0.6000,0.6429,0.5625,0.3197,0.2822 -0,struct_mrx_10_000030,0.8636,0.5335,0.3758,0.2426,0.8324,0.8305,0.2105,0.2105,0.2105,0.5263,0.5263,0.5263,0.9422,1.8050 -0,struct_mrx_10_000031,0.9111,0.5780,0.4363,0.2873,0.9060,0.8416,0.4571,0.4706,0.4444,0.7429,0.7647,0.7222,0.6226,0.5546 -0,struct_mrx_10_000032,0.9243,0.5522,0.4170,0.2688,0.9299,0.8426,0.4865,0.6000,0.4091,0.8108,1.0000,0.6818,1.0984,0.2958 -0,struct_mrx_10_000033,0.8469,0.5564,0.4029,0.2671,0.8196,0.7755,0.2308,0.2500,0.2143,0.5385,0.5833,0.5000,1.7926,1.7926 -0,struct_mrx_10_000034,0.8807,0.5654,0.4018,0.2625,0.8559,0.8527,0.4878,0.4762,0.5000,0.8293,0.8095,0.8500,0.2422,1.9284 -0,struct_mrx_10_000035,0.7775,0.3840,0.2953,0.1817,0.7877,0.7136,0.2500,0.2857,0.2222,0.6250,0.7143,0.5556,2.0376,2.3477 -0,struct_mrx_10_000036,0.7983,0.7181,0.5436,0.4620,0.6602,0.9019,0.2927,0.2308,0.4000,0.6342,0.5000,0.8667,0.6998,3.0696 -0,struct_mrx_10_000037,0.8544,0.7220,0.4849,0.3557,0.7616,0.8832,0.2353,0.1818,0.3333,0.6667,0.5152,0.9444,0.6748,2.6681 -0,struct_mrx_10_000038,0.9046,0.6131,0.4691,0.3190,0.8856,0.8498,0.4242,0.4118,0.4375,0.7879,0.7647,0.8125,0.4401,1.9598 -0,struct_mrx_10_000039,0.9071,0.6979,0.5199,0.3802,0.8222,0.8981,0.6667,0.6500,0.6842,0.7692,0.7500,0.7895,0.0234,0.0279 -0,struct_mrx_10_000040,0.8720,0.6833,0.5127,0.3784,0.7948,0.9065,0.3000,0.3000,0.3000,0.8500,0.8500,0.8500,1.6703,1.9348 -0,struct_mrx_10_000041,0.8274,0.6591,0.4606,0.3393,0.7169,0.9037,0.1000,0.1053,0.0952,0.7000,0.7368,0.6667,1.7103,1.7854 -0,struct_mrx_10_000042,0.7194,0.7938,0.4778,0.5080,0.4509,0.9123,0.2791,0.2308,0.3529,0.3721,0.3077,0.4706,2.5095,5.9609 -0,struct_mrx_10_000043,0.9324,0.6234,0.4684,0.3156,0.9078,0.8656,0.6857,0.7500,0.6316,0.8000,0.8750,0.7368,0.0656,0.0608 -0,struct_mrx_10_000044,0.7518,0.4696,0.3203,0.2131,0.6441,0.7993,0.0645,0.0769,0.0556,0.5161,0.6154,0.4444,2.0941,1.2582 -0,struct_mrx_10_000045,0.8429,0.5170,0.2777,0.1712,0.7361,0.8234,0.0784,0.0769,0.0800,0.4706,0.4615,0.4800,2.7145,3.4579 -0,struct_mrx_10_000046,0.7980,0.4761,0.3141,0.2000,0.7308,0.8031,0.2632,0.2778,0.2500,0.3158,0.3333,0.3000,3.4143,4.2879 -0,struct_mrx_10_000047,0.7679,0.7688,0.5019,0.4686,0.5404,0.9346,0.2143,0.1667,0.3000,0.3214,0.2500,0.4500,2.0947,5.4421 -0,struct_mrx_10_000048,0.8821,0.7361,0.6080,0.4775,0.8369,0.9278,0.2222,0.2353,0.2105,0.8333,0.8824,0.7895,0.9849,1.0192 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--- a/MIREX_data/2012/KSP1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4513,0.5120,0.4452,0.4978,0.4027,0.6587,0.3226,0.2273,0.5556,0.5161,0.3636,0.8889,0.3886,5.6940 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--- a/MIREX_data/2012/KSP2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4729,0.4694,0.5887,0.5978,0.5799,0.7253,0.3226,0.2273,0.5556,0.5161,0.3636,0.8889,0.2483,5.6940 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--- a/MIREX_data/2012/OYZS1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.7260,0.6138,0.6902,0.6038,0.8054,0.7574,0.1333,0.1667,0.1111,0.5333,0.6667,0.4444,7.7263,1.6833 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--- a/MIREX_data/2012/SMGA1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4686,0.8018,0.4364,0.8095,0.2988,0.7384,0.3077,0.2353,0.4444,0.4615,0.3529,0.6667,1.2538,8.7077 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--- a/MIREX_data/2012/SMGA2-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4586,0.8099,0.4236,0.8386,0.2834,0.7400,0.2222,0.1667,0.3333,0.4444,0.3333,0.6667,2.0566,10.0400 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--- a/MIREX_data/2012/SP1-per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4691,0.4286,0.5373,0.5119,0.5653,0.6687,0.3226,0.2273,0.5556,0.5161,0.3636,0.8889,0.3886,5.6940 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--- a/MIREX_data/2012/per_track_results.csv Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1001 +0,0 @@ -*Fold,Track,Normalised conditional entropy based over-segmentation score,Normalised conditional entropy based under-segmentation score,Frame pair clustering F-measure,Frame pair clustering precision rate,Frame pair clustering recall rate,Random clustering index,Segment boundary recovery evaluation measure @ 0.5sec,Segment boundary recovery precision rate @ 0.5sec,Segment boundary recovery recall rate @ 0.5sec,Segment boundary recovery evaluation measure @ 3sec,Segment boundary recovery precision rate @ 3sec,Segment boundary recovery recall rate @ 3sec,Median distance from an annotated segment boundary to the closest found boundary,Median distance from a found segment boundary to the closest annotated one -0,salami000000,0.4691,0.4286,0.5373,0.5119,0.5653,0.6687,0.3226,0.2273,0.5556,0.5161,0.3636,0.8889,0.3886,5.6940 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/collect_all_mirex_algo_output_data.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,80 @@ +function mrxoutput = collect_all_mirex_algo_output_data(base_directory, dsets, algos) +% function mrxoutput = collect_all_mirex_algo_output_data(base_directory, dsets, algos) +% +% GET ALL THE DATA! +% This function collects the output data (predictions of onset locations and segment +% labels) of the algorithms in the MIREX evaluation. +% +% BASE_DIRECTORY should be the "mirex_path" specified in "get_mirex_estimates.rb", +% or whatever directory contains all the downloaded MIREX data. For example: +% "/Users/jordan/Desktop/MIREX_data" +% +% DSETS should contain the names of the datasets. The default value is all of them: +% {'mrx09','mrx10_1','mrx10_2','sal'} +% Keep the DSETS in a consistent order across your work, because the index of the dataset +% is important for some of the other functions. +% +% ALGOS should contain the name of all the algorithms. The default value is all of them: +% {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'} +% As with the DSETS, keep these names consistent across all work. +% +% The output structure MRXOUTPUT contains the following fields: +% +% MRXOUTPUT(k).ALGO(j).SONG(i) gives the structure of the ith song of the kth dataset +% as predicted by the jth algorithm. +% +% SONG(i).TIM = onset times of annotation +% SONG(i).LAB = labels of sections +% SONG(i).FILE = file from which the above information derives +% +% Dependencies: +% - load_annotation.m + + +if nargin<1, + base_directory = '/Users/jordan/Desktop/MIREX_data'; +end +if nargin<2, + dsets = {'mrx09','mrx10_1','mrx10_2','sal'}; +end +if nargin<3, + algos = {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'}; +end + +% Use the CSV files to discover the names of all the songs. +csv_files = {}; +for i=1:length(dsets), + csv_files{end+1} = fullfile(base_directory,dsets{i},algos{1},'per_track_results.csv'); +end +fprintf('About to open some CSV files to extract the names of the songs in MIREX. If you see lots of errors, please ensure that the files exist in the correct location.\n') +for i=1:length(csv_files), + try + fid = fopen(csv_files{i}); + names_tmp = textscan(fid,'%s%s%*[^\n]','Delimiter',','); + fclose(fid); + year(i).names = names_tmp{2}(2:end); + catch + fprintf('Error opening or reading the following CSV file:\n %s\n',csv_files{i}); + end +end + + +fprintf('About to go through all the algorithm outputs and load all the predicted song descriptions. If you see lots of errors, please ensure that the files exist in the correct location.\n') + +mrxoutput = {}; +% For each dataset (DSET), and for each algorith (ALGO), look in turn at each song. +for k=1:length(dsets), + dset = dsets{k}; + for j=1:length(algos), + algo = algos{j}; + for i=1:length(year(k).names), + % FYI: PRED stands for 'prediction', in contrast to GT for 'ground truth'. + pred = fullfile(base_directory,dset,algo,strcat(year(k).names{i},'_pred.txt')); + [mrxoutput(k).algo(j).song(i).tim mrxoutput(k).algo(j).song(i).lab] = load_annotation(pred,'two_column'); + mrxoutput(k).algo(j).song(i).file = pred; + if isempty(mrxoutput(k).algo(j).song(i).tim), + fprintf('Screw up on %s?\n',mrxoutput(k).algo(j).song(i).file) + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/collect_all_mirex_annotations.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,108 @@ +function [data dset_origin] = collect_all_mirex_annotations(base_directory, dsets, algos) +% function [data dset_origin] = collect_all_mirex_annotations(base_directory, dsets, algos) +% +% GET ALL THE DATA! +% This function collects annotations from all years of MIREX evaluation. +% Annotation data (onsets and labels) all go in a single structure, including file +% locations. +% +% BASE_DIRECTORY should be the "mirex_path" specified in "get_mirex_estimates.rb", +% or whatever directory contains all the downloaded MIREX data. For example: +% "/Users/jordan/Desktop/MIREX_data" +% +% DSETS should contain the names of the datasets. The default value is all of them: +% {'mrx09','mrx10_1','mrx10_2','sal'} +% Keep the DSETS in a consistent order across your work, because the index of the dataset +% is important for some of the other functions. +% +% ALGOS should contain the name of at least one algorithm, so that the data can be +% accessed correctly. (Only the first algo is used, since the annotation is the same +% for each.) The default value is {'KSP1'}. +% +% The output DATA structure contains the following fields: +% +% DATA(i).TIM = onset times of annotation +% DATA(i).LAB = labels of sections +% DATA(i).FILE = file from which the above information derives +% DATA(i).DSET = numerical index of the dataset +% +% Dependencies: +% - load_annotation.m + +if nargin<1, + base_directory = '/Users/jordan/Desktop/MIREX_data'; +end + +% YEAR(i).NAMES will contain the NAMES of all the individual song files from YEAR i. +year = {}; + +% MRXTRUTH will contain an entry for every song in the MIREX evaluation, containing information about the +mrxtruth = {}; + +% Collect one CSV file from each year (the song names are identical in each algo CSV, so it is only necessary to have one valid ALGO here) +algos = {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'}; +dsets = {'mrx09','mrx10_1','mrx10_2','sal'}; +csv_files = {}; +for i=1:length(dsets), + csv_files{end+1} = fullfile(base_directory,dsets{i},algos{1},'per_track_results.csv'); +end + + +% Use the CSV files to discover the names of all the songs. +fprintf('About to open some CSV files to extract the names of the songs in MIREX. If you see lots of errors, please ensure that the files exist in the correct location.\n') +for i=1:length(csv_files), + try + fid = fopen(csv_files{i}); + names_tmp = textscan(fid,'%s%s%*[^\n]','Delimiter',','); + fclose(fid); + year(i).names = names_tmp{2}(2:end); + catch + fprintf('Error opening or reading the following CSV file:\n %s\n',csv_files{i}); + end +end + +% For every dataset (DSET), look through all the names (YEAR(k).NAMES), and load the annotation. +fprintf('About to load all the ground truth files published by MIREX. If you see lots of errors, please ensure that the files exist in the correct location, and that the function ''load_annotation'' exists.\n') +for k=1:length(dsets), + dset = dsets{k}; + algo = algos{1}; + for i=1:length(year(k).names), + % FYI: GT stands for 'ground truth', in contrast to PRED for 'prediction'. + gt = fullfile(base_directory,dset,algo,strcat(year(k).names{i},'_gt.txt')); + try + [mrxtruth(end+1).tim mrxtruth(end+1).lab] = load_annotation(gt,'two_column'); + mrxtruth(end).file = gt; + mrxtruth(end).dset = k; + if isempty(mrxtruth(end).tim), + % Sometimes the annotation might be empty! This can be bad news. + % If this happens, print out the name of the offending file, and delete this from the structure of annotations. + fprintf(mrxtruth(end).file) + fprintf('\n') + mrxtruth = mrxtruth(1:end-1); + end + catch + fprintf('Error opening the following ground truth file:\n %s\n',gt); + end + end +end + +% It can be useful to have a separate structure pointing to the index of the dataset. +% This is an optional output of the function. + +data = mrxtruth; +dset_origin = zeros(length(data),1); +for i=1:length(data), + dset_origin(i) = data(i).dset; +end + + +% Did this actually happen? That some of the onset times are in the incorrect order? +% Why yes, it did. It happens probably due to some floating point error... +% What you would see is two boundaries a tiny distance apart, where the later one +% appeared first, like: "145.0000468 silence; 145.0000000 end". +% In such cases, a perfectly acceptable fix is to just resort the times. They should +% be in sorted order anyway! + +for i=1:length(data), + data(i).tim = sort(data(i).tim); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/collect_all_mirex_results.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,57 @@ +function data = collect_all_mirex_results(base_directory, dsets, algos) +% function data = collect_all_mirex_results(base_directory, dsets, algos) +% GET ALL THE DATA! +% This function collects the evaluation results for all algorithms from all years of MIREX. +% It puts it all in a single structure. +% +% BASE_DIRECTORY should be the "mirex_path" specified in "get_mirex_estimates.rb", +% or whatever directory contains all the downloaded MIREX data. For example: +% "/Users/jordan/Desktop/MIREX_data" +% +% DSETS should contain the names of the datasets. The default value is all of them: +% {'mrx09','mrx10_1','mrx10_2','sal'} +% Keep the DSETS in a consistent order across your work, because the index of the dataset +% is important for some of the other functions. +% +% ALGOS should contain the name of all the algorithms. The default value is all of them: +% {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'} +% As with the DSETS, keep these names consistent across all work. +% +% The output DATA structure contains the following fields: +% +% DATA(k).ALGO(j).RESULTS is a matrix giving the results for the kth dataset and +% the jth algorithm. + +if nargin<1, + base_directory = '/Users/jordan/Desktop/MIREX_data'; +end +if nargin<2, + dsets = {'mrx09','mrx10_1','mrx10_2','sal'}; +end +if nargin<3, + algos = {'KSP1','KSP2','KSP3','MHRAF1','OYZS1','SBV1','SMGA1','SMGA2','SP1'}; +end + +% Create output structure DATA. +% DATA(k).ALGO(j).RESULTS will contain the results attained by algorithm J for all songs in dataset K. +data = {}; + +% For every dataset (DSET), look at every algorithm (ALGO), and load the CSV file containing all the results. +for k=1:length(dsets), + dset = dsets{k}; + for j=1:length(algos), + algo = algos{j}; + % Build the name of the CSV file: + csv_file = fullfile(base_directory,dset,algo,'per_track_results.csv'); + fid = fopen(csv_file); + % Read it, assuming the first line is a header: + data_tmp = textscan(fid,'%n%s%n%n%n%n%n%n%n%n%n%n%n%n%n%n','Delimiter',',','HeaderLines',1); + fclose(fid); + data(k).algo(j).names = data_tmp{2}; + data(k).algo(j).results = zeros(size(data_tmp{1},1),length(data_tmp)-2); + % The first two columns contain a dummy variable and the name of the song, respectively. Ignore these. + for i=3:length(data_tmp) + data(k).algo(j).results(:,i-2) = data_tmp{i}; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/collect_all_public_annotations.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,208 @@ +function [publictruth dset_origin] = collect_all_public_annotations(base_directory) +% function function [data dset_origin] = collect_all_public_annotations(base_directory) +% +% GET ALL THE DATA! +% This function collects annotations from many public repositories of structural analyses. +% Annotation data (onsets and labels) all go in a single structure, including file +% locations. Refer to the README file to see what these repositories are and how to +% download them. +% +% BASE_DIRECTORY should be the "mirex_path" specified in "get_mirex_estimates.rb", +% or whatever directory contains all the downloaded MIREX data. For example: +% "/Users/jordan/Desktop/MIREX_data" +% +% Before running this script, you must have downloaded the original repositories +% to the "mirex_path" directory, and unzipped them. If you did that, then this script +% should be able to find and interpret all the annotations. (Except for those in .xml +% format, which should be pre-processed. Again, refer to the README.) +% +% The output DATA structure contains the following fields for the ith song: +% +% DATA(i).TIM = onset times of annotation +% DATA(i).LAB = labels of sections +% DATA(i).FILE = file from which the above information derives +% DATA(i).DSET = numerical indices of the main dataset (e.g., QM, RWC, etc.) and the +% subset (e.g., within QM: 'CaroleKing', 'Queen', etc.) +% +% Dependencies: +% - load_annotation.m + +if nargin<1, + base_directory = '/Users/jordan/Desktop/MIREX_data' +end + +public_dir = fullfile(base_directory,'public_data'); + +% Assemble lists of all the directories where the data live. This section is very hacky!!! + +% RWC +rwc_dirs = {fullfile(public_dir,'AIST.RWC-MDB-C-2001.CHORUS'), fullfile(public_dir,'AIST.RWC-MDB-G-2001.CHORUS'), fullfile(public_dir,'AIST.RWC-MDB-J-2001.CHORUS'), fullfile(public_dir,'AIST.RWC-MDB-P-2001.CHORUS')}; + +% QM, i.e., Isophonics data from Queen Mary +qm_dirs = {fullfile(public_dir,'Carole%20King%20Annotations'), fullfile(public_dir,'Michael%20Jackson%20Annotations'), fullfile(public_dir,'Queen%20Annotations'), fullfile(public_dir,'The%20Beatles%20Annotations'), fullfile(public_dir,'Zweieck%20Annotations')}; + +% EP, i.e., data released by Ewald Peiszer +ep_dir = fullfile(public_dir,'ep_groundtruth_txt/groundtruth'); +% Or, you could download the original data, and convert the XML files to LAB files using +% the Ruby script xml2lab.rb. +% ep_dir = fullfile(public_dir,'ep_groundtruth/groundtruth'); + +% IRISA +irisa_dirs = {fullfile(public_dir,'IRISA.RWC-MDB-P-2001.BLOCKS'), fullfile(public_dir,'IRISA.RWC-MDB-P-2012.SEMLAB_v003_reduced'), fullfile(public_dir,'IRISA.RWC-MDB-P-2012.SEMLAB_v003_full')}; + +% TUT Beatles +fullfile(public_dir,'TUT','*'); +[tmp tutfiles] = fileattrib(fullfile(public_dir,'TUT','*')); +tut_dirs = {}; +for i=1:length(tutfiles), + if tutfiles(i).directory==1, + tut_dirs{end+1} = tutfiles(i).Name; + end +end + +% UPF Beatles +upf_dirs = {fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/01_-_Please_please_me_1963'), fullfile(public_dir,' /Users/jordan/Desktop/MIREX_data/public_data/02_-_With_The_Beatles_1963'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/03_-_A_hard_days_night_1964'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/04_-_Beatles_for_sale_1964'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/05_-_Help_1965'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/06_-_Rubber_Soul'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/07_-_Revolver'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/08_-_Sgt._Pepper''s_Lonely_Hearts_Club_Band'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/09_-_Magical_Mystery_Tour'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/10_-_The_Beatles\ \(White\ Album\)\ CD1'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/10_-_The_Beatles\ \(White\ Album\)\ CD2'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/11_-_Abbey_Road'), fullfile(public_dir,'/Users/jordan/Desktop/MIREX_data/public_data/12_-_Let_it_Be')}; + +% SALAMI +salami_dir = fullfile(public_dir,'SALAMI_data_v1.2/data'); + +dset_origin = []; + +publictruth = {}; + +% Load RWC data +for i=1:length(rwc_dirs), + [tmp all_files tmp1] = fileattrib(strcat(rwc_dirs{i},'/*')); + for j=1:length(all_files), + if all_files(j).directory==0, + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'lab'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 1 i]; % NB: This '1' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf('The following file appears to be empty:\n %s\n',publictruth(end).file) + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + % NB: many flags will be thrown here because many of the RWC files are empty. + end + end + end +end + +% Load Isophonics data +for i=1:length(qm_dirs), + tmp_dir_name = fullfile(qm_dirs{i},'seglab','*'); + [tmp all_files tmp1] = fileattrib(tmp_dir_name); + for j=1:length(all_files), + [tmp1 tmp2 tmp_file_extension] = fileparts(all_files(j).Name); + if all_files(j).directory==0 & all_files(j).GroupRead==1 & isequal(tmp_file_extension,'.lab'), + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'lab'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 2 i]; % NB: This '2' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf('The following file appears to be empty:\n %s\n',publictruth(end).file) + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + end + end + end +end + +% Load EP data +[tmp all_files tmp1] = fileattrib(strcat(ep_dir,'/*.txt')); +for j=1:length(all_files), + if all_files(j).directory==0 & all_files(j).GroupRead==1, + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'two_column'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 3 1]; % NB: This '3' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf(publictruth(end).file) + fprintf('\n') + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + end + end +end + +% Load IRISA data +for i=1:length(irisa_dirs), + [tmp all_files tmp1] = fileattrib(strcat(irisa_dirs{i},'/*.lab')); + for j=1:length(all_files), + if all_files(j).directory==0 & all_files(j).GroupRead==1, + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'lab'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 4 i]; % NB: This '4' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf('The following file appears to be empty:\n %s\n',publictruth(end).file) + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + end + end + end +end + +% Load TUT data +for i=1:length(tut_dirs), + [tmp all_files tmp1] = fileattrib(strcat(tut_dirs{i},'/*.lab')); + for j=1:length(all_files), + if all_files(j).directory==0 & all_files(j).GroupRead==1, + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'lab'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 5 i]; % NB: This '5' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf('The following file appears to be empty:\n %s\n',publictruth(end).file) + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + end + end + end +end + +% Load SALAMI data +[tmp all_files tmp1] = fileattrib(strcat(salami_dir,'/*')); +for j=1:length(all_files), + if all_files(j).directory == 0 & all_files(j).GroupRead==1, + if strcmp(all_files(3).Name(end-12:end),'uppercase.txt'), + try + [publictruth(end+1).tim publictruth(end+1).lab] = load_annotation(all_files(j).Name,'two_column'); + publictruth(end).file = all_files(j).Name; + dset_origin = [dset_origin; 6 1]; % NB: This '6' is HARD-CODED. + if isempty(publictruth(end).tim), + fprintf('The following file appears to be empty:\n %s\n',publictruth(end).file) + publictruth = publictruth(1:end-1); + dset_origin = dset_origin(1:end-1,:); + end + catch + fprintf('Error opening or reading the following file. (It might be empty, or not a song file.)\n %s\n',all_files(j).Name); + end + end + end +end + +% Would you believe that in some of the annotations, two times are in the wrong order? It is simply appalling. +% We fix this here. +for i=1:length(publictruth), + if ~isequal(publictruth(i).tim,sort(publictruth(i).tim)), + publictruth(i).tim = sort(publictruth(i).tim); + fprintf('Fixed order of time points in this file:%s\n',publictruth(i).file) + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/compile_datacubes.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,183 @@ +function [datacube newcube extracube indexing_info] = compile_datacubes(mirex_truth, mirex_dset_origin, public_truth, mirex_output, mirex_results, mir2pub) +% function [datacube newcube extracube indexing_info] = compile_datacubes(mirex_truth, ... +% mirex_dset_origin, public_truth, mirex_output, mirex_results, mir2pub) +% % % % % % % % % LOAD DATA % % % % % % % % % +% +% +% We have now loaded all the data we could possibly be interested in. (* Almost; see below.) +% But it is not in the easiest form to process. I.e., it is not in a big matrix. +% So the idea now is to assemble DATACUBES with all the data we are interested in. +% Each DATACUBE will have three dimensions: +% DATACUBE(i,j,k) +% will store the performance on song i, according to metric j, by algorithm k. +% The index of the song (i) is its position in the giant matrix MIREX_TRUTH of loaded +% ground truth files published by MIREX. +% The index of the metrics (j) is determined by the MIREX spreadsheet. (The fields are +% summarized below.) The matrix itself will not have a 'header' row, so you must +% look at this script to know what is what. +% The index of the algorithms (k) is determined by how the algorithms were loaded. + +% % % % % % % % % % % COLLECT DATACUBES % % % % % % % % % % % % % + + +% In this script, the 'n' at the beginning of a variable means 'number of'. +n_songs = size(mir2pub,1); +n_metrics = size(mirex_results(1).algo(1).results,2); +n_algos = size(mirex_results(1).algo,2); +datacube = zeros(n_songs, n_metrics, n_algos); + +% DATACUBE contains evaluation data published by MIREX: +topelement = 1; +for i=1:length(mirex_results), + for j=1:9, + datacube(topelement:topelement-1+length(mirex_results(i).algo(j).results),:,j) = mirex_results(i).algo(j).results; + end + topelement = topelement + length(mirex_results(i).algo(j).results); +end + +fprintf('Collecting new data about annotations......') + +% NEWCUBE contains data related to properties of the estimated and annotated descriptions: +newcube = zeros(size(datacube,1),12,size(datacube,3)); +% For each song, +for i=1:size(datacube,1), + % Collect information specific to the annotation: + song_length = mirex_truth(i).tim(end)-mirex_truth(i).tim(1); + n_segs_ann = length(mirex_truth(i).tim)-1; % number of segments in the annotation + n_labs_ann = length(unique(mirex_truth(i).lab))-1; % number of labels in the annotation + mean_seg_len_ann = song_length/n_segs_ann; % average length of segments in the annotation + n_segs_per_lab_ann = n_segs_ann/n_labs_ann; % number of segments per label + % We now want to look up the algorithm output corresponding to this MIREX annotation. + % Unforunately, the indexing is tricky. The MIREX annotations are indexed 1 to 1497 (unless + % you changed the defaults), whereas the MIREX algorithm output is sorted by dataset and + % re-indexed: e.g., the 298th annotation is actually the 1st song of dataset 2. + % So we need to do a little archaeology to get I_WRT_DSET, the index with respect to + % the dataset. + dset = mirex_dset_origin(i); + tmp = find(mirex_dset_origin==dset); + i_wrt_dset = find(tmp==i); + % You can test that this indexing worked by looking at the file names of the two descriptions: + % mirex_truth(i).file + % mirex_output(dset).algo(1).song(i_wrt_dset).file + for j=1:9, + % Collect information specific to the estimated description: + n_segs_est = length(mirex_output(dset).algo(j).song(i_wrt_dset).tim) - 1; + n_labs_est = length(unique(mirex_output(dset).algo(j).song(i_wrt_dset).lab)) - 1; + mean_seg_len_est = song_length/n_segs_est; + n_segs_per_lab_est = n_segs_est/n_labs_est; + overseg_bound = n_segs_est-n_segs_ann; % Direct measure of oversegmentation (too many sections) by estimate + overseg_label = n_labs_est-n_labs_ann; % Direct measure of overdiscrimination (too many label types) by estimate + % Record all this new data to the NEWCUBE: + newcube(i,:,j) = [dset, song_length, n_segs_ann, n_labs_ann, mean_seg_len_ann, n_segs_per_lab_ann, ... + n_segs_est, n_labs_est, mean_seg_len_est, n_segs_per_lab_est, overseg_bound, overseg_label]; + end +end + +fprintf('Done!\nConducting new evaluation of MIREX data.......') + +% EXTRACUBE contains recalculations of the metrics using our own evaluation package. +% Computing the metrics takes a little while. +extracube = zeros(size(datacube,1),24,size(datacube,3)); +tic +for i=1:size(datacube,1) + dset = mirex_dset_origin(i); + tmp = find(mirex_dset_origin==dset); + i_wrt_dset = find(tmp==i); + % Get onsets and labels from Annotation: + a_onset = mirex_truth(i).tim; + a_label = mirex_truth(i).lab; + for j=1:9, + % Get onsets and labels from Estimated description: + e_onset = mirex_output(dset).algo(j).song(i_wrt_dset).tim; + e_label = mirex_output(dset).algo(j).song(i_wrt_dset).lab; + [tmp labres segres] = compare_structures(e_onset, e_label, a_onset, a_label); + extracube(i,:,j) = [labres segres]; + end + % It can be nice to see a progress meter... It took me about 30 seconds to compute 100 songs, and there are ~1500 songs. + if mod(i,100)==0, + toc + fprintf('Getting there. We have done %i songs so far.\n',i) + end +end +fprintf('Done!\nJust tidying up now.......') +% You can wind up with NaNs. Get rid of them please. +extracube(isnan(extracube)) = 0; + + +% You might think we are done, but we are not! We now create a few handy vectors to remind +% us of what these metrics actually are. There is so much data... hard to keep track of it all. +% +% Datacube columns (14, 1-14): +% "S_o,S_u,pw_f, pw_p, pw_r, rand, bf1, bp1, br1, bf6, bp6, br6, mt2c, mc2t" +% That is: +% 1,2 oversegmentation and undersegmentation scores +% 3,4,5 pairwise f-measure, precision and recall +% 6 Rand index +% 7,8,9 boundary f-measure, precision and recall with a threshold of 0.5 seconds +% 10,11,12 boundary f-measure, precision and recall with a threshold of 3 seconds +% 13,14 median true-to-claim and claim-to-true distances +% +% Newcube columns (12, 15-26): dset, song length, +% n_segs_ann, n_labs_ann, mean_seg_len_ann, n_segs_per_lab_ann, +% n_segs_est, n_labs_est, mean_seg_len_est, n_segs_per_lab_est, +% overseg_bound, overseg_label +% +% ExtraCube columns (24, 27-50): +% vector_lab = [pw_f, pw_p, pw_r, K, asp, acp, I_AE, H_EA, H_AE, S_o, S_u, rand]; +% vector_seg = [mt2c, mc2t, m, f, d_ae, d_ea, b_f1, b_p1, b_r1, b_f6, b_p6, b_r6]; +% That is, for the labelling metrics: +% 1,2,3 pairwise f-measure, precision and recall +% 4,5,6 K, average speaker purity, average cluster purity +% 7,8,9 mutual information and both conditional entropies +% 10,11 oversegmentation and undersegmentation scores +% 12 Rand index; +% And the boundary metrics: +% 1,2 median true-to-claim and claim-to-true distances +% 3,4 missed boundaries and fragmentation scores +% 5,6 directional hamming distance and inverse directional hamming distance +% 7,8,9 boundary f-measure, precision and recall with a threshold of 0.5 seconds +% 10,11,12 boundary f-measure, precision and recall with a threshold of 3 seconds + +column_labels = ... + ... % Datacube: + {'S_o','S_u','pw_f','pw_p','pw_r', ... + 'rand','bf1','bp1','br1','bf6', ... + ... + 'bp6','br6','mt2c','mc2t',... + ... % Newcube: + 'ds', ... + 'len','nsa','nla','msla','nspla', ... + ... + 'nse','nle','msle','nsple','ob', ... + 'ol',... + ... % Extracube: + 'pw_f','pw_p','pw_r','K', ... + ... + 'asp','acp','I_AE','H_EA','H_AE', ... + 'S_o','S_u','rand','mt2c','mc2t', ... + ... + 'm','f','d_ae','d_ea','b_f1', ... + 'b_p1','b_r1','b_f6','b_p6','b_r6'}; + +% It is actually nice, for later on, to have these formatted slightly more prettily. +% Also, we would rather retain '1-f' and '1-m' than f and m, so we make this switch now: +extracube(:,[15, 16],:) = 1-extracube(:,[15, 16],:); +column_labels = {'S_O','S_U','pw_f','pw_p','pw_r','Rand','bf_{.5}','bp_{.5}','br_{.5}','bf_3','bp_3','br_3','mt2c','mc2t','ds','len','ns_a','nl_a','msl_a','nspl_a','ns_e','nl_e','msl_e','nspl_e','ob','ol','pw_f_x','pw_p_x','pw_r_x','K','asp','acp','I_AE_x','H_EA_x','H_AE_x','S_o_x','S_u_x','rand','mt2c_x','mc2t_x','1-m','1-f','d_ae_x','d_ea_x','b_f1_x','b_p1_x','b_r1_x','b_f6_x','b_p6_x','b_r6_x'}; + +% We will manually create lists of indices (with matching labels) for the sets of metrics we want to examine. +% The indices are into MEGACUBE, which is the concatenation of DATACUBE (mirex evaluation), NEWCUBE (extra parameters based on mirex data), and EXTRACUBE (my computations: K, asp, acp, etc.). +sind_manual1 = [3,6,30,5,1,31,4,2,32]; % pwf, rand, K, pwp, S_O, acp, pwr, S_U, asp +sind_manual2 = [10,7,11,8,42,14,12,9,41,13]; % bf3, bf.5, bp3, bp.5, f, mc2tm br3m br.5, m, mt2c +sind_manual3 = [16:26]; % len, nsa, nla, msla, nspla, nse, nle, msle, nsple, ob, ol +indexing_info(1).manual_set = sind_manual1; +indexing_info(2).manual_set = sind_manual2; +indexing_info(3).manual_set = sind_manual3; +for i=1:3, + indexing_info(i).labels = column_labels(indexing_info(i).manual_set); + indexing_info(i).all_labels = column_labels; +end + +% Running this script is time-consuming and tedious. Once again, we save our work. +save('./datacubes','datacube','newcube','extracube','indexing_info') + +fprintf('Finished! Goodbye.\n') \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/do_correlation3.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,111 @@ +function [asig pval a a_] = do_correlation(megacube, songs, metrics, algos, algo_groups, merge_algos, merge_songs, merge_dsets, metric_labels, bonferroni) + +% function [asig pval a a_] = do_correlation(megacube, songs, metrics, algos, algo_groups, merge_algos, merge_songs, merge_dsets, metric_labels, bonferroni) +% +% Script to make and analyze correlation plot. +% Example usage: +% To run your first experiment (Fig 1a) request: +% do_correlation(megacube, lab_measures, sind_manual1, [1:9], -1, 0, 1, -1, s_manual1) +% +% MEGACUBE is the giant (N songs) x (M metrics) x (L algorithms) matrix of evaluation results. +% SONGS, METRICS and ALGOS are the indices into these three dimensions desired. +% ALGO_GROUPS indicates groups of algorithms that should be averaged together rather than counted separately. +% (this has not yet been implemented) +% Set MERGE_ALGOS > 0 in order to compute the median score across algorithms. +% Set MERGE_SONGS > 0 in order to compute the median score across songs. +% MERGE_DSETS is also not yet implemented. +% METRIC_LABELS is a matrix of strings, one for each of the METRICS, for use in plotting. +% Set BONFERRONI > 0 in order to apply a bonferroni correction of BONFERRONI. (Default value: 0.05.) +% Note a few hard-coded decisions, such as: +% - significance level hard coded as 0.05. +% - in the image, decision that tau > 0.8 is strong, tau > 0.33 is weak, and tau < 0.33 is nothing. + +% Defaults and hard coding values: +if nargin<10, + bonferroni = 0.05; +end +significant_p = 0.05; +maxtau = 0.8; +mintau = 0.33; + + + +tmpcube = megacube(songs,metrics,algos); + +% if exist('algo_groups'), +% for i=1:length(algo_groups), +% merge the groups somehow... +% end +% end + +if merge_algos>0, % If we merge algorithms, take the median score across algorithms. + tmpcube = median(tmpcube,3); +elseif merge_songs>0, % If we merge songs, take the median score across songs. + tmpcube = median(tmpcube,1); % Then, resize the matrix to be 2-d: + tmpcube = transpose(reshape(tmpcube,size(tmpcube,2),size(tmpcube,3))); +end + +% Compute Kendall tau correlation: +[a pval] = corr(tmpcube,'type','Kendall'); +% Apply bonferroni correction: +m = length(a)*(length(a)-1)/2; +asig = pval<significant_p; +if bonferroni>0, + fprintf('Bonferroni applied.\n') + asig = (pval*m)<bonferroni; % This is the matrix of values that are significant. +end +a_ = (abs(a)>=maxtau) + (abs(a)>=mintau); +a_ = tril(a_,-1); + +% A contains the correlation values themselves. +% ASIG is a binary matrix that states whether the correlation is statistically significant. +% A_ is a matrix of -2, -1, 0, 1 and 2s that says whether a correlation is qualitatively strong (2), qualitatively weak (1), or nada (0). +% Sometimes values will be statistically significant, but qualitatively insignificant. We do not want to bother looking at these, so +% let us make our pretty picture carefully. + +% The values we display will always be straight from A. The colour we display, to emphasize the strong correlations, +% should be the element-wise product of A, ASIG, and A_. +% Also: +% Iff tau>0.33 (a_>0), include text. +% Iff tau is significant (asig=1), include background. +% Iff tau>0.8 (a_=2), put in bold. +% Iff tau>0.8 AND tau is significant, invert the color of the text (because the colour will be darker). + +img = a_.*a.*asig; +img = img(2:end,1:end-1); % ignore the diagonal +clf +imagesc(img, [-1 1]) +for i=1:length(a_), + for j=1:length(a_), + if a_(i,j)>0, + % tau is >0.33 so we definitely write the value. need to determine fontface and colour. + % if tau>.8, put in bold + if abs(a_(i,j))>1, + fontw = 'bold'; + else + fontw = 'normal'; + end + if abs(a_(i,j))>1 & asig(i,j)==1, + textcolor = [1 1 1]; + else + textcolor = [0 0 0]; + end + % h = text(j-.35,i-1,num2str(a(i,j),2),'Color',textcolor); + h = text(j,i-1,sprintf('%.2f',a(i,j)),'Color',textcolor,'FontWeight',fontw,'FontSize',12,'HorizontalAlignment','center'); + set(h,'HorizontalAlignment','center') + end + end +end +cmap_el = transpose([linspace(.3,1,50)]); +cmap = repmat(cmap_el,1,3); +cmap = [cmap; flipud(cmap)]; +% Alternatively: +cmap = [ones(size(cmap_el)) cmap_el cmap_el; flipud([cmap_el cmap_el ones(size(cmap_el))])]; +colormap(cmap); + +set(gca,'YTickLabel',metric_labels(2:end),'YTick',(1:length(a)-1),'FontAngle','italic','FontSize',12) +set(gca,'XTickLabel',metric_labels(1:end-1),'XTick',(1:length(a)-1),'FontAngle','italic','FontSize',12) +% set(gcf,'Position',[1000,1000,700,300]) +% set(gca,'XTickLabel',metric_labels(2:2:end),'YTick',(1:length(a)/2)) + +% axis([0.5, length(a)-.5, 1.5, length(a)+.5]) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/do_correlation3_fig3_only.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,110 @@ +function [asig pval a a_] = do_correlation3_fig3_only(megacube, songs, metrics, algos, algo_groups, merge_algos, merge_songs, merge_dsets, metric_labels, bonferroni, metrics2, metric_labels2) + +% Script to make and analyze correlation plot. +% Example usage: +% To run your first experiment (Fig 1a) request: +% do_correlation(megacube, lab_measures, sind_manual1, [1:9], -1, 0, 1, -1, s_manual1) +% +% Note a few hard-coded decisions, such as: +% - use of 0.05 significance level with Bonferroni correction +% - in the image, decision that tau > 0.8 is strong, tau > 0.4 is weak, and tau < 0.4 is nothing. + +maxtau = 0.8; +mintau = 0.33; + + + +tmpcube1 = megacube(songs,[metrics metrics2],algos); +tmpcube2 = megacube(songs,metrics2,algos); + + +if merge_algos>0, % If we merge algorithms, take the median score across algorithms. + tmpcube1 = median(tmpcube1,3); + tmpcube2 = median(tmpcube2,3); +elseif merge_songs>0, % If we merge songs, take the median score across songs. + tmpcube1 = median(tmpcube1,1); + tmpcube2 = median(tmpcube2,1); + tmpcube1 = transpose(reshape(tmpcube1,size(tmpcube1,2),size(tmpcube1,3))); + tmpcube2 = transpose(reshape(tmpcube2,size(tmpcube2,2),size(tmpcube2,3))); +end + + +% Accept a matrix and its pvalues, determine which values are significant. +% Matrix is A, pvalues are PVAL +tic +[a pval] = corr(tmpcube2, tmpcube1,'type','Kendall'); +toc +% Apply bonferroni correction: +m = sum(sum(tril(ones(size(a)), length(metrics)-1))) +asig = pval<0.05; +if bonferroni==1, + fprintf('Bonferroni applied.\n') + asig = (pval*m)<0.05; % This is the matrix of values that are significant. +end + +% Make a pretty picture: +a_ = (abs(a)>=maxtau) + (abs(a)>=mintau); +a_ = tril(a_,length(metrics)-1); +% bg = 2*triu(ones(size(a_))); + + +% A contains the correlation values themselves. +% ASIG is a binary matrix that states whether the correlation is statistically significant. +% A_ is a matrix of -2, -1, 0, 1 and 2s that says whether a correlation is qualitatively strong (2), qualitatively weak (1), or nada (0). + +% The values we display will always be straight from A. The image we display, though, to emphasize the strong correlations, +% should be the element-wise product of A, ASIG, and A_. + +% So we will only display colours for values that are statistically significant. +% In addition, we will only put in inverted text those that are qualitatively large (>0.8). +% However, this leaves the possibility of large correlations (>0.8) that are insignificant, which show up as white text on white background. +% Therefore, let us change tacks: +% +% If tau>0.33 (a_>0), include text. +% If tau is significant (asig=1), include background. +% If tau>0.8 (a_=2), put in bold. +% If tau>0.8 AND significant, invert the color of the text. + +img = a_.*a.*asig; +img = img(:,1:end-1); +figure,imagesc(img, [-1 1]) +for i=1:size(a_,1), + for j=1:size(a_,2), + if a_(i,j)>0, + % tau is >0.33 so we definitely write the value. need to determine fontface and colour. + % if tau>.8, put in bold + if abs(a_(i,j))>1, + fontw = 'bold'; + else + fontw = 'normal'; + end + if abs(a_(i,j))>1 & asig(i,j)==1, + textcolor = [1 1 1]; + else + textcolor = [0 0 0]; + end + % h = text(j-.35,i,num2str(a(i,j),2),'Color',textcolor); + h = text(j,i,sprintf('%.2f',a(i,j)),'Color',textcolor,'FontWeight',fontw,'FontSize',8,'HorizontalAlignment','center'); + set(h,'HorizontalAlignment','center','Rotation',90) + end + end +end +cmap_el = transpose([linspace(.3,1,50)]); +cmap = repmat(cmap_el,1,3); +cmap = [cmap; flipud(cmap)]; +% Alternatively: +cmap = [ones(size(cmap_el)) cmap_el cmap_el; flipud([cmap_el cmap_el ones(size(cmap_el))])]; +colormap(cmap); + +set(gca,'YTickLabel',metric_labels2,'YTick',1:size(a,1),'FontAngle','italic','FontSize',10) +% set(gca,'XTickLabel',[metric_labels metric_labels2],'XTick',1:size(a,2),'FontAngle','italic','FontSize',10) + +set(gca,'XTickLabel',[],'XTick',1:size(a,2)-1); +t = text((1:size(a,2)-1)-.5,size(a,1)*ones(1,size(a,2)-1)+.7,[metric_labels metric_labels2(1:end-1)]); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation',90,'FontAngle','italic'); + + +% set(gcf,'Position',[1000,1000,700,300]) +% set(gca,'XTickLabel',metric_labels(2:2:end),'YTick',(1:length(a)/2)) + +% axis([0.5, length(a)-.5, 1.5, length(a)+.5]) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/do_correlation_analyses.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,524 @@ +% Assuming we have followed the factory settings so far, we now have four datasets, +% and a whole lot of evaluation metrics. But note that in one evaluation (no. 2, mrx10_1), +% we do not want to consider any metrics related to labels, since the ground truth in this +% case had arbitrary labels. (It was done using boundary-only IRISA annotations.) +% So, we make two sets of indices, LAB_MEASURES and SEG_MEASURES. They are handy. +lab_measures = ismember(mirex_dset_origin,[1 3 4]); +seg_measures = ismember(mirex_dset_origin,[1 2 3 4]); + +% Now we can do our correlation studies! +% First, generate figure 1a. For that, we call the function DO_CORRELATION. +% Type HELP DO_CORRELATION to understand what all the arguments mean... The short of it +% is that we select the songs, metrics and algorithms to compare, and then choose +% whether to take the median across all songs or across all algorithms. + +[asig pval a a_] = do_correlation3(megadatacube, lab_measures, indexing_info(1).manual_set, [1:9],... + 0, 0, 1, 0, indexing_info(1).labels, 0.05); +saveas(gcf,'./plots/fig1a.jpg') + +[asig pval a a_] = do_correlation3(megadatacube, lab_measures, indexing_info(1).manual_set, [1:9],... + 0, 1, 0, 0, indexing_info(1).labels, 0.05); +saveas(gcf,'./plots/fig1b.jpg') + +[asig pval a a_] = do_correlation3(megadatacube, seg_measures, indexing_info(2).manual_set, [1:9],... + 0, 0, 1, 0, indexing_info(2).labels, 0.05); +saveas(gcf,'./plots/fig2a.jpg') + +[asig pval a a_] = do_correlation3(megadatacube, seg_measures, indexing_info(2).manual_set, [1:9],... + 0, 1, 0, 0, indexing_info(2).labels, 0.05); +saveas(gcf,'./plots/fig2b.jpg') + +[asig pval a a_] = do_correlation3_fig3_only(megadatacube, lab_measures, [indexing_info(1).manual_set indexing_info(2).manual_set], [1:9], 0, 1, 0, 0, indexing_info(2).all_labels([indexing_info(1).manual_set indexing_info(2).manual_set]), 1, indexing_info(3).manual_set, indexing_info(3).labels); +saveas(gcf,'./plots/fig3.jpg') + + +do blah +% % % % % % % % % % % % The rest of this is still under construction, so I have inserted an error in the previous line to halt the script. + +% Are the trends qualitatively similar across datasets? +% Fig 1a +figure,[asig pval a a_] = do_correlation3(megadatacube, lab_measures, indexing_info(1).manual_set, [1:9], -1, 0, 1, -1, indexing_info(1).labels, 1); +figure,[asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,1), indexing_info(1).manual_set, [1:9], -1, 0, 1, -1, indexing_info(1).labels, 1); +figure,[asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,3), indexing_info(1).manual_set, [1:9], -1, 0, 1, -1, indexing_info(1).labels, 1); +figure,[asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,4), indexing_info(1).manual_set, [1:9], -1, 0, 1, -1, indexing_info(1).labels, 1); +% Fig 1b +figure, [asig pval a a_] = do_correlation3(megadatacube, lab_measures, sind_manual1, [1:9], -1, 1, 0, -1, indexing_info(1).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,1), indexing_info(1).manual_set, [1:9], -1, 1, 0, -1, indexing_info(1).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,3), indexing_info(1).manual_set, [1:9], -1, 1, 0, -1, indexing_info(1).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,4), indexing_info(1).manual_set, [1:9], -1, 1, 0, -1, indexing_info(1).labels, 1); +% Fig 2a +figure, [asig pval a a_] = do_correlation3(megadatacube, seg_measures, sind_manual2, [1:9], -1, 0, 1, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,1), indexing_info(2).manual_set, [1:9], -1, 0, 1, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,2), indexing_info(2).manual_set, [1:9], -1, 0, 1, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,3), indexing_info(2).manual_set, [1:9], -1, 0, 1, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,4), indexing_info(2).manual_set, [1:9], -1, 0, 1, -1, indexing_info(2).labels, 1); +% Fig 2b +figure, [asig pval a a_] = do_correlation3(megadatacube, seg_measures, sind_manual2, [1:9], -1, 1, 0, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,1), indexing_info(2).manual_set, [1:9], -1, 1, 0, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,2), indexing_info(2).manual_set, [1:9], -1, 1, 0, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,3), indexing_info(2).manual_set, [1:9], -1, 1, 0, -1, indexing_info(2).labels, 1); +figure, [asig pval a a_] = do_correlation3(megadatacube, ismember(mirex_dset_origin,4), indexing_info(2).manual_set, [1:9], -1, 1, 0, -1, indexing_info(2).labels, 1); + + +% "Does this indicate that the algorithms are better at boundary precision than recall? In fact, the opposite is the case: average bp6 bp.5 was simply consistently worse for most algorithms." +% For all algos: +mean(median(megadatacube(:,sind_manual2,:),3),1) +% For each algo: +mean(megadatacube(:,sind_manual2,:),1) + + +H = boxplot(megadatacube(:,[17 21],:)) + +tmp = sort(megadatacube(:,17,:)); +tmp2 = sort(megadatacube(:,21,:)); +tmp2(round(length(tmp2)/4),:,:), tmp2(round(length(tmp2)*3/4),:,:) + +tmp2 = sort(tmp2(:)); +tmp2(round(length(tmp2)/4)), tmp2(3*round(length(tmp2)/4)) + + +% % % % % % % % % % % ENd OF REAL WORK AREA % % % % % % % % % % % % % + + +clf,imagesc(a.*(abs(a)>.7)) +set(gca,'XTickLabel',[],'XTick',(1:50)-.5) +set(gca,'YTickLabel',s,'YTick',(1:50)) +t = text((1:50)-.5,51*ones(1,50),s); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation',90); +hold on +for i=1:9, + plot([0 50],[i*5 i*5],'w') + plot([i*5 i*5],[0 50],'w') +end + +% a = corr([datacube(1:300,:,1) newcube(1:300,:,1) newmetriccube(1:300,:,1)]); + +a = corr([datacube(lab_measures,:,1) newcube(lab_measures,:,1) newmetriccube(lab_measures,:,1)]); +b = corr([datacube(seg_measures,:,1) newcube(seg_measures,:,1) newmetriccube(seg_measures,:,1)]); + +% Look at label measures only in this case. +imagesc(sortrows(transpose(sortrows((abs(a)>0.7))))) +[t1 t2] = (sortrows(transpose(sortrows((abs(a)>0.7))))); + + +b = zeros(size(a)); +for j=[3,4,5,6,7,9], + b = b+corr([datacube(:,:,j) newcube(:,:,j) newmetriccube(:,:,j)]); +end +b=b/6; + + +% Look at correlations among all figures, but pay attention to pvalues too. +% Only plot those less than 0.05, with conservative bonferroni correction. +megadatacube_l = [datacube(lab_measures,:,:) newcube(lab_measures,:,:) newmetriccube(lab_measures,:,:)]; +megadatacube_s = [datacube(seg_measures,:,:) newcube(seg_measures,:,:) newmetriccube(seg_measures,:,:)]; +% megadatacube_l = median(megadatacube_l(:,use_these_labels,:),3); +% megadatacube_s = median(megadatacube_s(:,use_these_segs,:),3); + + + +megadatacube_all = median(megadatacube_l(:,[use_these_labels use_these_segs use_these_extras],:),3); +megadatacube_all(:,16:17) = 1 - megadatacube_all(:,16:17); +[al pval] = corr(megadatacube_all); +m = length(al)*(length(al)-1)/2; +imagesc(al.*((pval*m)<0.05)) +al_ = al.*((pval*m)<0.05); +al_ = tril(al_ .* (abs(al_)>.5)); +imagesc(al_) +for i=1:length(al_), + for j=1:length(al_), + if (al_(i,j)~=0) & (i~=j), + text(j-.35,i,num2str(al_(i,j),2)) + end + end +end +% [bl pvbl] = corr(megadatacube_all,'type','Kendall'); +m = length(bl)*(length(bl)-1)/2; +imagesc(bl.*((pvbl*m)<0.05)) +bl_ = bl.*((pvbl*m)<0.05); +bl_ = tril(bl_) % .* (abs(bl_)>.0)); +imagesc(bl_) +for i=1:length(bl_), + for j=1:length(bl_), + if (bl_(i,j)~=0) & (i~=j), + text(j-.35,i,num2str(bl_(i,j),2)) + end + end +end + +% Or, we could do this: Take all the computed Kendall taus, i.e., the non-diagonal elements of bl. +taus = bl(find(bl<1)); +taus = taus-mean(taus); +taus = taus/std(taus); +P = normcdf(-abs(taus)); +ind = find(P<=0.05); +taus = bl(find(bl<1)); +taus(ind) + +c = colormap; +c(32,:) = [1 1 1]; +c(31,:) = [1 1 1]; +c = min(1,c*1.6); +colormap(c) +set(gca,'XTickLabel',[],'XTick',(1:length(al_))-.4) +set(gca,'YTickLabel',s([use_these_labels use_these_segs use_these_extras]),'YTick',(1:length(al_))) +t = text((1:length(al_))-.3,(length(al_)+1)*ones(1,length(al_))+.3,s([use_these_labels use_these_segs use_these_extras])); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation',90); +axis([0 31 0 31]) +saveas(gcf,'./plots/all_correlations.jpg') + +s = {'S_o','S_u','pw_f','pw_p','pw_r','rand','bf1','bp1','br1','bf6','bp6','br6','mt2c','mc2t','ds','len','nsa','nla','msla','nspla','nse','nle','msle','nsple','ob','ol','pw_f_x','pw_p_x','pw_r_x','K','asp','acp','I_AE_x','H_EA_x','H_AE_x','S_o_x','S_u_x','rand','mt2c_x','mc2t_x','m','f','d_ae_x','d_ea_x','b_f1_x','b_p1_x','b_r1_x','b_f6_x','b_p6_x','b_r6_x'}; +s_type = [1,2,3,1,2,3,6,4,5,6,4,5,4,5, 7,7,7,7,7,7,7,7,7,7,7,7,3,1,2,3,2,1,3,1,2,1,2, 3,4,5,5,4,7,7,3,1,2,3,1,2]; +megadatacube_s(:,40:41,:) = 1 - megadatacube_s(:,40:41,:); +megadatacube_s(:,51,:) = 2*megadatacube_s(:,38,:).*megadatacube_s(:,39,:)./(megadatacube_s(:,38,:)+megadatacube_s(:,39,:)); +% This makes a new 51st metric which is a combination of m and f. +s_type(51) = 6; +s{51} = 'mf'; + + +% [a pval] = corr(median([datacube(lab_measures,:,1) newcube(lab_measures,:,1) newmetriccube(lab_measures,:,1)],3)); +[a pval] = corr(mean(megadatacube_l,3)); +m = length(a)*(length(a)-1)/2; +imagesc(a.*((pval*m)<0.05)) +a_ = a.*((pval*m)<0.05); +c = colormap; +c(32,:) = [1 1 1]; +colormap(c) + +% I want to make a claim about song length correlating to the algorithms or not. Let us make sure it is valid across all algorithms, and is not just applicable to the median: +for j=1:9, + a = corr([datacube(lab_measures,:,j) newcube(lab_measures,:,j) newmetriccube(lab_measures,:,j)]); + a(16,[17 19 21 23]) +end + +% BoxPlot of the number of segments in each algorithm output +boxplot(reshape(newcube(:,7,:),[length(newcube),9,1])) + +% Look at best 10 and worst 10 songs in each dataset, according to PW_F metric. +% Average results across algorithms for this one. +unique_algorithms = [3 4 5 6 7]; +tmp = datacube; +tmp(:,:,3) = mean(tmp(:,:,[1:3,9]),3); +tmp(:,:,7) = mean(tmp(:,:,7:8),3); +tmp = mean(tmp(lab_measures,:,unique_algorithms),3); +[tmp1 order] = sortrows(tmp,-3); +order1 = lab_measures(order); +pub_songids = X.mir2pub(order1); +values = tmp1((pub_songids>0),3); +filenames = {}; +for i=1:length(pub_songids), + if pub_songids(i)>0, + filenames{end+1} = X.pubanns(pub_songids(i)).file; + end +end + +mirid = pub2mir(336); +make_structure_image(mirid, miranns, MD, mirdset, X, MR) +saveas(gcf,'./plots/MJ_dont_care.jpg') +make_structure_image(121, miranns, MD, mirdset, X, MR) +saveas(gcf,'./plots/play_the_game.jpg') + +% Plot difficulty by album: + + +genres = {}; +subgenres = {}; +issalami = zeros(length(filenames),1); +for i=1:length(filenames), + file = filenames{i}; + if strfind(file,'SALAMI_data'), + issalami(i)=1; + salami_id = file(79:85); + salami_id = salami_id(1:strfind(salami_id,'/')-1); + salami_row = find(aaux.metadata{1}==str2num(salami_id)); + genres{end+1} = cell2mat(aaux.metadata{15}(salami_row)); + subgenres{end+1} = cell2mat(aaux.metadata{16}(salami_row)); + end +end +gs = grp2idx(genres); +subgs = grp2idx(subgenres); +boxplot(values(find(issalami)),transpose(genres)) +axis([0.5 5.5 0 1]) +saveas(gcf,'salami_breakdown.png') +boxplot(values(find(issalami)),transpose(subgenres),'colors',cmap(round(gs*63/6),:),'orientation','horizontal') + +[tmp1 tmp2] = hist(subgs,max(subgs)-1); +tmp1 = find(tmp1>5); % do these subgenres only +tmp1 = ismember(subgs,tmp1); +tmp2 = find(issalami); +boxplot(values(tmp2(tmp1)),transpose(subgenres(tmp1)),'colors',cmap(round(gs(tmp1)*63/6),:),'orientation','horizontal') + + + + + +% Look at scatter plots so that we can qualitatively attribute the correlations to things (e.g., low-precision variance). +tmpcube = mean(datacube,3); +for i=1:4, + for j=i+1:5, + subplot(5,5,i+(j-1)*5) + scatter(tmpcube(:,i),tmpcube(:,j),'x') + end +end + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +% Now again, we will want to run the correlation study by taking medians across algorithms (do the metrics rank the songs the same way?) and medians across songs (do the metrics rank the algorithms the same way?). + +% Take the label metrics only, take median across songs: +% tmpcube = median(megadatacube_l(:,sind_manual1,:),1); +% tmpcube = transpose(reshape(tmpcube,size(tmpcube,2),size(tmpcube,3))); +% [a pval] = corr(tmpcube,'type','Kendall'); +% m = length(a)*(length(a)-1)/2; +% a.*((pval*m)<0.05); % This is the matrix of values that are significant. +% Alternatively, we can plot all the metrics, treat them as random normal variables, and select only those that stand out. + + + +% [asig pval a] = do_correlation(megadatacube, songs, metrics, algos, algo_groups, merge_algos (1 = do, 0 = do not), merge_songs, merge_dsets, metric_labels) +[asig pval a] = do_correlation(megadatacube, lab_measures, sind_manual1, [1:9], -1, 0, 1, -1, s_manual1) +[asig pval a] = do_correlation(megadatacube, lab_measures, [use_these_labels use_these_segs], [1:9], -1, 0, 1, -1, s([use_these_labels use_these_segs])) + +[asig pval a] = do_correlation(megadatacube, lab_measures, [1:12], [1:9], -1, 0, 1, -1, s(1:12)) + + +[a pval] = corr(megadatacube_l(:,:,1),'type','Kendall'); + + + +% Take the label metrics only, take median across algorithms: +tmpcube = median(megadatacube_l(:,sind_manual1,:),3); +[a pval] = corr(tmpcube); %,'type','Kendall'); +m = length(a)*(length(a)-1)/2; +a.*((pval*m)<0.05); % This is the matrix of values that are significant. +% However, with so many data points (over 1400) it is very easy to be significant... + + + + +imagesc(a.*((pval*m)<0.05)) +al_ = al.*((pval*m)<0.05); +al_ = tril(al_ .* (abs(al_)>.5)); +imagesc(al_) +for i=1:length(al_), + for j=1:length(al_), + if (al_(i,j)~=0) & (i~=j), + text(j-.35,i,num2str(al_(i,j),2)) + end + end +end + + +clf,imagesc(a.*(abs(a)>.7)) +set(gca,'XTickLabel',[],'XTick',(1:50)-.5) +set(gca,'YTickLabel',s,'YTick',(1:50)) +t = text((1:50)-.5,51*ones(1,50),s); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation',90); +hold on +for i=1:9, + plot([0 50],[i*5 i*5],'w') + plot([i*5 i*5],[0 50],'w') +end + +% a = corr([datacube(1:300,:,1) newcube(1:300,:,1) extracube(1:300,:,1)]); + +a = corr([datacube(lab_measures,:,1) newcube(lab_measures,:,1) extracube(lab_measures,:,1)]); +b = corr([datacube(seg_measures,:,1) newcube(seg_measures,:,1) extracube(seg_measures,:,1)]); + +% Look at label measures only in this case. +imagesc(sortrows(transpose(sortrows((abs(a)>0.7))))) +[t1 t2] = (sortrows(transpose(sortrows((abs(a)>0.7))))); + + +b = zeros(size(a)); +for j=[3,4,5,6,7,9], + b = b+corr([datacube(:,:,j) newcube(:,:,j) extracube(:,:,j)]); +end +b=b/6; + + +% Look at correlations among all figures, but pay attention to pvalues too. +% Only plot those less than 0.05, with conservative bonferroni correction. +megadatacube_l = [datacube(lab_measures,:,:) newcube(lab_measures,:,:) extracube(lab_measures,:,:)]; +megadatacube_s = [datacube(seg_measures,:,:) newcube(seg_measures,:,:) extracube(seg_measures,:,:)]; +% megadatacube_l = median(megadatacube_l(:,use_these_labels,:),3); +% megadatacube_s = median(megadatacube_s(:,use_these_segs,:),3); + + + +megadatacube_all = median(megadatacube_l(:,[use_these_labels use_these_segs use_these_extras],:),3); +megadatacube_all(:,16:17) = 1 - megadatacube_all(:,16:17); +[al pval] = corr(megadatacube_all); +m = length(al)*(length(al)-1)/2; +imagesc(al.*((pval*m)<0.05)) +al_ = al.*((pval*m)<0.05); +al_ = tril(al_ .* (abs(al_)>.5)); +imagesc(al_) +for i=1:length(al_), + for j=1:length(al_), + if (al_(i,j)~=0) & (i~=j), + text(j-.35,i,num2str(al_(i,j),2)) + end + end +end +% [bl pvbl] = corr(megadatacube_all,'type','Kendall'); +m = length(bl)*(length(bl)-1)/2; +imagesc(bl.*((pvbl*m)<0.05)) +bl_ = bl.*((pvbl*m)<0.05); +bl_ = tril(bl_) % .* (abs(bl_)>.0)); +imagesc(bl_) +for i=1:length(bl_), + for j=1:length(bl_), + if (bl_(i,j)~=0) & (i~=j), + text(j-.35,i,num2str(bl_(i,j),2)) + end + end +end + +% Or, we could do this: Take all the computed Kendall taus, i.e., the non-diagonal elements of bl. +taus = bl(find(bl<1)); +taus = taus-mean(taus); +taus = taus/std(taus); +P = normcdf(-abs(taus)); +ind = find(P<=0.05); +taus = bl(find(bl<1)); +taus(ind) + +c = colormap; +c(32,:) = [1 1 1]; +c(31,:) = [1 1 1]; +c = min(1,c*1.6); +colormap(c) +set(gca,'XTickLabel',[],'XTick',(1:length(al_))-.4) +set(gca,'YTickLabel',s([use_these_labels use_these_segs use_these_extras]),'YTick',(1:length(al_))) +t = text((1:length(al_))-.3,(length(al_)+1)*ones(1,length(al_))+.3,s([use_these_labels use_these_segs use_these_extras])); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation',90); +axis([0 31 0 31]) +saveas(gcf,'./plots/all_correlations.jpg') + +s = {'S_o','S_u','pw_f','pw_p','pw_r','rand','bf1','bp1','br1','bf6','bp6','br6','mt2c','mc2t','ds','len','nsa','nla','msla','nspla','nse','nle','msle','nsple','ob','ol','pw_f_x','pw_p_x','pw_r_x','K','asp','acp','I_AE_x','H_EA_x','H_AE_x','S_o_x','S_u_x','rand','mt2c_x','mc2t_x','m','f','d_ae_x','d_ea_x','b_f1_x','b_p1_x','b_r1_x','b_f6_x','b_p6_x','b_r6_x'}; +s_type = [1,2,3,1,2,3,6,4,5,6,4,5,4,5, 7,7,7,7,7,7,7,7,7,7,7,7,3,1,2,3,2,1,3,1,2,1,2, 3,4,5,5,4,7,7,3,1,2,3,1,2]; +megadatacube_s(:,38:39,:) = 1 - megadatacube_s(:,38:39,:); +megadatacube_s(:,51,:) = 2*megadatacube_s(:,38,:).*megadatacube_s(:,39,:)./(megadatacube_s(:,38,:)+megadatacube_s(:,39,:)); +% This makes a new 51st metric which is a combination of m and f. +s_type(51) = 6; +s{51} = 'mf'; + + +% [a pval] = corr(median([datacube(lab_measures,:,1) newcube(lab_measures,:,1) extracube(lab_measures,:,1)],3)); +[a pval] = corr(mean(megadatacube_l,3)); +m = length(a)*(length(a)-1)/2; +imagesc(a.*((pval*m)<0.05)) +a_ = a.*((pval*m)<0.05); +c = colormap; +c(32,:) = [1 1 1]; +colormap(c) + +% I want to make a claim about song length correlating to the algorithms or not. Let us make sure it is valid across all algorithms, and is not just applicable to the median: +for j=1:9, + a = corr([datacube(lab_measures,:,j) newcube(lab_measures,:,j) extracube(lab_measures,:,j)]); + a(16,[17 19 21 23]) +end + +% BoxPlot of the number of segments in each algorithm output +boxplot(reshape(newcube(:,7,:),[length(newcube),9,1])) + +% Look at best 10 and worst 10 songs in each dataset, according to PW_F metric. +% Average results across algorithms for this one. +unique_algorithms = [3 4 5 6 7]; +tmp = datacube; +tmp(:,:,3) = mean(tmp(:,:,[1:3,9]),3); +tmp(:,:,7) = mean(tmp(:,:,7:8),3); +tmp = mean(tmp(lab_measures,:,unique_algorithms),3); +[tmp1 order] = sortrows(tmp,-3); +order1 = lab_measures(order); +pub_songids = X.mir2pub(order1); +values = tmp1((pub_songids>0),3); +filenames = {}; +for i=1:length(pub_songids), + if pub_songids(i)>0, + filenames{end+1} = public_truth(pub_songids(i)).file; + end +end + +mirid = pub2mir(336); +make_structure_image(mirid, mirex_truth, mirex_output, mirex_dset_origin, X, mirex_results) +saveas(gcf,'./plots/MJ_dont_care.jpg') +make_structure_image(121, mirex_truth, mirex_output, mirex_dset_origin, X, mirex_results) +saveas(gcf,'./plots/play_the_game.jpg') + +% Plot difficulty by album: + + +genres = {}; +subgenres = {}; +issalami = zeros(length(filenames),1); +for i=1:length(filenames), + file = filenames{i}; + if strfind(file,'SALAMI_data'), + issalami(i)=1; + salami_id = file(79:85); + salami_id = salami_id(1:strfind(salami_id,'/')-1); + salami_row = find(aaux.metadata{1}==str2num(salami_id)); + genres{end+1} = cell2mat(aaux.metadata{15}(salami_row)); + subgenres{end+1} = cell2mat(aaux.metadata{16}(salami_row)); + end +end +gs = grp2idx(genres); +subgs = grp2idx(subgenres); +boxplot(values(find(issalami)),transpose(genres)) +axis([0.5 5.5 0 1]) +saveas(gcf,'salami_breakdown.png') +boxplot(values(find(issalami)),transpose(subgenres),'colors',cmap(round(gs*63/6),:),'orientation','horizontal') + +[tmp1 tmp2] = hist(subgs,max(subgs)-1); +tmp1 = find(tmp1>5); % do these subgenres only +tmp1 = ismember(subgs,tmp1); +tmp2 = find(issalami); +boxplot(values(tmp2(tmp1)),transpose(subgenres(tmp1)),'colors',cmap(round(gs(tmp1)*63/6),:),'orientation','horizontal') \ No newline at end of file
--- a/get_mirex_data.m Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,54 +0,0 @@ -function [data, hoos, header] = get_mirex_data(year, dataset) - -% function [data header]= get_mirex_data(year, dataset) -% -% Seeks and loads MIREX data for the desired YEAR (and, if applicable, -% DATASET). The year options are 9, 10, 11 and 12. For 10 and 11, the -% eligible DATASET values are 9 or 10. -% -% Also returns the labels for the metrics in HEADER. - -year_s = int2str(year+2000); -if nargin>1, - dataset_s = strcat('\',int2str(2000+dataset),'data'); -end - -dir = strcat('H:\My Documents\Dropbox\Work\mirex_metaanalysis\MIREX_data\',year_s); -if nargin>1, - dir = strcat(dir,dataset_s); -end -[tmp, files] = fileattrib(strcat(dir,'\*')); - -data = []; -hoos = []; -for i=1:5, - fid = fopen(files(i).Name,'r'); - one_data = textscan(fid,'%n%n%n%n%n%n%n%n%n%n%n%n%n%n%n%n%n','delimiter',',','Headerlines',1,'CollectOutput',1); - fclose(fid); - one_data = one_data{1}(:,2:end-2); - hoos = [hoos; ones(size(one_data,1),1)*i]; - data = [data; one_data]; -end - -switch year - case 9 - header = {'overSegScore', 'underSegScore', 'pwF', 'pwPrecision',... - 'pwRecall', 'R', 'Fmeasure@0.5s', 'precRate@0.5s', 'recRate@0.5s',... - 'medianTrue2claim@0.5s', 'medianClaim2true@0.5s', 'Fmeasure@3s',... - 'precRate@3s', 'recRate@3s'}; - case 12 - header = {'Normalised conditional entropy based over-segmentation score', ... - 'Normalised conditional entropy based under-segmentation score', ... - 'Frame pair clustering F-measure',... - 'Frame pair clustering precision rate',... - 'Frame pair clustering recall rate', ... - 'Random clustering index',... - 'Segment boundary recovery evaluation measure @ 0.5sec',... - 'Segment boundary recovery precision rate @ 0.5sec', ... - 'Segment boundary recovery recall rate @ 0.5sec',... - 'Segment boundary recovery evaluation measure @ 3sec', ... - 'Segment boundary recovery precision rate @ 3sec',... - 'Segment boundary recovery recall rate @ 3sec', ... - 'Median distance from an annotated segment boundary to the closest found boundary',... - 'Median distance from a found segment boundary to the closest annotated one'}; -end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/match_mirex_to_public_data.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,114 @@ +function [pub2mir, mir2pub, P] = match_mirex_to_public_data(mirex_truth, public_truth, mirex_dset_origin, public_dset_origin, mir2pub_relevance) +% function [pub2mir, mir2pub] = match_mirex_to_public_data(mirex_truth, +% public_truth, mirex_dset_origin, public_dset_origin, mir2pub_relevance) +% +% This function looks through the collections of MIREX and PUBLIC annotations +% and attempts to find matches between them, i.e., possible public annotations +% that could be the same as the MIREX annotations. +% +% MIREX_TRUTH and PUBLIC_TRUTH are Nx1 and Mx1 structures where N and M are the number +% of MIREX and PUBLIC annotations available, respectively. Each element contains fields +% TIM, LAB and DSET which give the time points and labels of the annotation, as well as +% the index of the dataset. +% +% MIREX_DSET_ORIGIN and PUBLIC_DSET_ORIGIN contain the same information in the DSET +% field, but in an array. +% +% The output vectors PUB2MIR and MIR2PUB work in the following way. If the nth MIREX and +% the mth PUBLIC annotations are found to match, then PUB2MIR(m) = n and MIR2PUB(n) = m. +% +% MIR2PUB_RELEVANCE contains a simple Px2 array where each row contains (1) the index of +% a MIREX dataset and (2) the index of a PUBLIC dataset that are hypothesized to contain +% some of the same songs. Including this cuts down on the number of datasets that are +% searched for matches. +% +% For example, if the default values are kept in all the other scripts, then the correct +% relevance matches are: +% +% mir2pub_relevance = [1 2; 1 3; 1 5; 2 1; 2 4; 3 1; 3 4; 4 1; 4 2; 4 3; 4 4; 4 5; 4 6]; +% +% That was based on the following assumptions: +% public_dset_origin: 1 = RWC [AIST], 2 = Isophonics, 3 = EP, 4 = IRISA [Euro and RWC], 5 = TUT, 6 = SALAMI +% mirex_dset_origin: 1 = 09 [Isophonics, Beatles, EP], 2 = 10a [RWC, boundaries only], 3 = 10b [RWC AIST], 4 = 12 [salami] + + +pub2mir = zeros(length(public_truth),1); +mir2pub = zeros(length(mirex_truth),1); + +% public_dset_origin: 1 = RWC [AIST], 2 = Isophonics, 3 = EP, 4 = IRISA [Euro and RWC], 5 = TUT, 6 = SALAMI +% mirex_dset_origin: 1 = 09 [Isophonics, Beatles, EP], 2 = 10a [RWC, boundaries only], 3 = 10b [RWC AIST], 4 = 12 [salami] + +% Look through all the MIREX annotations. For each one, look at the public annotations available. +% When you find a song which has the same length (within a second), compare the structures and save the output. + +% We shall do this dataset by dataset. Starting with 2009, with first select the relevant datasets from the MIREX and public domains. +rel(1).rel_mir = find(mirex_dset_origin==1); +rel(1).rel_pub = find(public_dset_origin(:,1)==2 | public_dset_origin(:,1)==3 | public_dset_origin(:,1)==5); +rel(2).rel_mir = find(mirex_dset_origin==2); +rel(2).rel_pub = find(public_dset_origin(:,1)==4); +rel(3).rel_mir = find(mirex_dset_origin==3); +rel(3).rel_pub = find(public_dset_origin(:,1)==1); +rel(4).rel_mir = find(mirex_dset_origin==4); +rel(4).rel_pub = find(public_dset_origin(:,1)==6); + +metrics = [2 2 2 2]; +quality_threshes = [.99 0.99 0.99 0.99]; + +for K=1:4, + rel_mir = rel(K).rel_mir; + rel_pub = rel(K).rel_pub; + quality_thresh = quality_threshes(K); + + % We maintain a list of the songs that have not been matched yet: + unmatched_mirdata = rel_mir; + unmatched_pubdata = rel_pub; + + % We also make a matrix to hold the match between all the songs. + pwf = zeros(length(rel_mir),length(rel_pub)); + + % Run the follow script, optionally several times with increasing values of length_thresh to search more widely. + % (We reduce the search space each time, so using a longer threshold becomes more and more feasible on later interations.) + length_thresh = 5; + [mir2pub pub2mir pwf] = match_mirex_to_public_data_macro(mir2pub, pub2mir, pwf, mirex_truth, public_truth, rel_mir, rel_pub, length_thresh, quality_thresh); + length_thresh = 10; + [mir2pub pub2mir pwf] = match_mirex_to_public_data_macro(mir2pub, pub2mir, pwf, mirex_truth, public_truth, rel_mir, rel_pub, length_thresh, quality_thresh); + length_thresh = 15; + [mir2pub pub2mir pwf] = match_mirex_to_public_data_macro(mir2pub, pub2mir, pwf, mirex_truth, public_truth, rel_mir, rel_pub, length_thresh, quality_thresh); + + % The variable P will contain the quality of the matches between all the songs tested. + P(K).pwf = pwf; +end + +% That was a lot of searching... We do not want to do it twice! Save the output. +save('./match_mirex_to_public_data_results','pub2mir','mir2pub','P'); + + +% % Bonus work for Table 2: +% How many MIREX songs did I find a match for in each category? +fprintf('MIREX dataset......number of pieces.....number identified\n\n') +for K=1:4, + % This is the number of MIREX songs identified with public annotations. + tmp = sum(mir2pub(find(mirex_dset_origin==K))>0); + fprintf('Dataset %i .. %i .. %i\n',K,length(find(mirex_dset_origin==K)),tmp) +end +% Aslo, how many public annotations did I find a match for? +for K=1:6, + % This is the number of public songs that occurred in MIREX. + sum(pub2mir(find(public_dset_origin(:,1)==K))>0) +end +% +% mir2pub(find(mirex_dset_origin==2)) +% +% % Confirm that the songs we are matching are actually the same: +% mir_id = find(pub2mir,1); % find the first matching public song +% pub_id = mir2pub(mir_id); +% mirex_truth(mir_id).tim +% public_truth(pub_id).tim +% % Are they the same? If not, something is going wrong! +% +% % How to identify a MIREX song based on its public match: +% % Thankfully, we retained the filenames of the public data. +% mir_id = find(pub2mir,1); % find the first matching public song +% pub_id = mir2pub(mir_id); +% mirex_truth(mir_id).file +% public_truth(pub_id).file \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/match_mirex_to_public_data_macro.m Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,54 @@ +function [mir2pub pub2mir pwf] = match_mirex_to_public_data_macro(mir2pub, pub2mir, pwf, miranns, pubanns, rel_mir, rel_pub, length_thresh, quality_thresh) + +tic +unmatched_mirdata = find(max(transpose(pwf))<.99); +unmatched_pubdata = find(max(pwf)<.99); +% This is how many more songs we have to match. +length(unmatched_mirdata) +if ~isempty(unmatched_mirdata) & ~isempty(unmatched_pubdata), + + for i=row(unmatched_mirdata), + dothese = find(max(pwf)<.99 & pwf(i,:)==0); + for j=dothese, + if (abs(miranns(rel_mir(i)).tim(end)-pubanns(rel_pub(j)).tim(end)) < length_thresh), + res = boundary_grader(miranns(rel_mir(i)).tim, pubanns(rel_pub(j)).tim, 1, 3); + pwf(i,j) = res(1); + end + end + toc + end +end + +% After running that, we can update our matches. +% We will choose, for each MIREX song, the best matching public annotation as long as it has a pwf value of at least 0.99. +% (If we just picked the best one, it obviously might be a non-match.) +% We will also choose the best-matching MIREX song for each public annotation in the same manner. This means that the best +% match from mir2pub and from pub2mir could be different! This is expected, since any song by the Beatles will show up +% only once in MIREX, but could show up several times in the public data. + +% Recall: +% rel_mir lists the indices (in miranns) of the mir songs we are looking at. +% rel_pub lists the indices (in pubanns) of the pub songs we are looking at. +% We want to find, for each miranns index, which pubanns index matches it. + +% Pick best public match for each MIREX annotation: +for i=1:length(rel_mir), + miranns_id = rel_mir(i); + [value rel_pub_id] = max(pwf(i,:)); + if value>=quality_thresh, + pubanns_id = rel_pub(rel_pub_id); + mir2pub(miranns_id) = pubanns_id; + pub2mir(pubanns_id) = miranns_id; + end +end + +% Pick best MIREX match for each public annotation: +for i=1:length(rel_pub), + pubanns_id = rel_pub(i); + [value rel_mir_id] = max(pwf(:,i)); + if value>=quality_thresh, + miranns_id = rel_mir(rel_mir_id); + pub2mir(pubanns_id) = miranns_id; + mir2pub(miranns_id) = pubanns_id; + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.txt Fri Sep 20 16:36:45 2013 +0100 @@ -0,0 +1,61 @@ +This README explains all the steps required to replicate the work done for the article, "A meta-analysis of the MIREX Structural Segmentation task." + +All of the contents of this repository are released under the MIT license (shown below). + + + +===== HOW TO REPRODUCE THE PAPER ===== + +1: You will need Ruby and Matlab and a connection to the Internet. + +2: You will need to download a version of the Structural Analysis Evaluation project, also hosted on SoundSoftware. You can donwload it here: +<https://code.soundsoftware.ac.uk/projects/structural_analysis_evaluation/repository> + +3: You will need to edit some of the Ruby and Matlab files you have downloaded, in order to point the program to the desired folders. + + + +2. +- Download data from MIREX website: + - Ground truth files + - Algorithm output + - Reported evaluation results + +File: get_mirex_estimates.rb +Instructions: set local directory for download. Download repositories, unzip, set directories. [Can I write the script to do this?] + +(Pre-process this data.) + +- Assemble MIREX ground truth file data in Matlab. +- Assemble MIREX algorithm output data in Matlab. +- Assemble MIREX evaluation results in Matlab. + +- Download public repositories of annotations. + +- Assemble public ground truth data in Matlab. + +- Compute extra evaluation measures using MIREX algorithm output. + +- Compute extra features of the annotations (song length, mean segment length, etc.). + +- Compute correlations between all these parameters. + +- Display correlation figures. + +- Search for matches between MIREX and public ground truth. + +- Display analysis result figure. + + + + + +The MIT License (MIT) + +Copyright (c) 2013 DDMAL + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file
--- a/script.m Thu Mar 28 02:50:18 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,16 +0,0 @@ -% cd H:\My Documents\Dropbox\Work\mirex_metaanalysis -% base_dir = '\MIREX_data'; -% years = {'/2009', '/2010', '/2011', '/2012'}; -% filenames = ls(strcat('.',base_dir,years{i})); -% d(i).results = ... - -% , 'medianTrue2claim@3s',... -% 'medianClaim2true@3s'}; -% The last two two metrics are redundant, since median distances do not -% depend on the threshold chosen. - -[data, hoos, header] = get_mirex_data(12); -rho = corr(data); -imagesc(rho) - -anovan(data(:,3),hoos); \ No newline at end of file