Mercurial > hg > segmentation
changeset 4:56a2ca9359d0
evaluation script
| author | mitian |
|---|---|
| date | Wed, 08 Apr 2015 11:47:47 +0100 |
| parents | bac230fcd7bd |
| children | 237799544386 |
| files | SegEval.py cnmf.py utils/SegUtil.py |
| diffstat | 3 files changed, 149 insertions(+), 62 deletions(-) [+] |
line wrap: on
line diff
--- a/SegEval.py Tue Apr 07 17:58:37 2015 +0100 +++ b/SegEval.py Wed Apr 08 11:47:47 2015 +0100 @@ -48,7 +48,7 @@ # Algorithm params h = 8 # Size of median filter for features in C-NMF R = 15 # Size of the median filter for the activation matrix C-NMF -rank = 8 # Rank of decomposition for the boundaries +rank = 4 # Rank of decomposition for the boundaries rank_labels = 6 # Rank of decomposition for the labels R_labels = 6 # Size of the median filter for the labels # Foote @@ -122,7 +122,7 @@ self.bpmBands = [30, 45, 60, 80, 100, 120, 180, 240, 400, 600] '''Peak picking settings for novelty based method''' - self.threshold = 50 + self.threshold = 30 self.confidence_threshold = 0.5 self.delta_threshold = 0.0 self.backtracking_threshold = 1.9 @@ -139,9 +139,9 @@ def pairwiseF(self, annotation, detection, tolerance=3.0, combine=1.0, idx2time=None): '''Pairwise F measure evaluation of detection rates.''' - if idx2time: + if idx2time != None: # Map detected idxs to real time - detection = [idx2time[int(np.rint(i))] for i in detection] + [ao.gt[-1]] + detection = [idx2time[int(np.rint(i))] for i in detection] + [annotation[-1]] # print 'detection', detection detection = np.append(detection, annotation[-1]) res = EvalObj() @@ -178,7 +178,7 @@ res.F = 2 * res.P * res.R / (res.P + res.R) return res - def wirteIndividualHeader(self, filename): + def writeIndividualHeader(self, filename): '''Write header of output files for individual features.''' with open(filename, 'a') as f: @@ -190,7 +190,7 @@ 'timbre_F_3', 'timbre_AD_3', 'timbre_DA_3', 'tempo_tp_05', 'tempo_fp_05', 'tempo_fn_05', 'tempo_P_05', 'tempo_R_05', 'tempo_F_05', 'tempo_AD_05', 'tempo_DA_05', \ 'tempo_tp_3', 'tempo_fp_3', 'tempo_fn_3', 'tempo_P_3', 'tempo_R_3', 'tempo_F_3', 'tempo_AD_3', 'tempo_DA_3']) - def wirteIndividualRes(self, filename, ao_name, gt_res_05, gt_res_3, harmonic_res_05, harmonic_res_3, timbre_res_05, timbre_res_3, tempo_res_05, tempo_res_3): + def writeIndividualRes(self, filename, ao_name, gt_res_05, gt_res_3, harmonic_res_05, harmonic_res_3, timbre_res_05, timbre_res_3, tempo_res_05, tempo_res_3): '''Write result of single detection for individual features.''' with open(filename, 'a') as f: @@ -208,25 +208,39 @@ with open(filename, 'a') as f: csvwriter = csv.writer(f, delimiter=',') csvwriter.writerow(['audio', 'gt_tb_P_0.5', 'gt_tb_R_0.5', 'gt_tb_F_0.5', 'gt_tb_P_3', 'gt_tb_R_3', 'gt_tb_F_3', 'gt_tp_P_0.5', 'gt_tp_R_0.5', 'gt_tp_F_0.5', 'gt_tp_P_3', 'gt_tp_R_3', 'gt_tp_F_3',\ - 'gt_hm_P_0.5', 'gt_hm_R_0.5', 'gt_hm_F_0.5', 'gt_hm_P_3', 'gt_hm_R_3', 'gt_hm_F_3', 'tb_tp_P_0.5', 'tb_tp_R_0.5', 'tb_tp_F_0.5', 'tb_tp_P_3', 'tb_tp_R_3', 'tb_tp_F_3', 'tb_hm_P_0.5', 'tb_hm_R_0.5', 'tb_hm_F_0.5', \ - 'tb_hm_P_3', 'tb_hm_R_3', 'tb_hm_F_3', 'tp_hm_P_0.5', 'tp_hm_R_0.5', 'tp_hm_F_0.5', 'tp_hm_P_3', 'tp_hm_R_3', 'tp_hm_F_3', 'gt_tb_tp_P_0.5', 'gt_tb_tp_R_0.5', 'gt_tb_tp_F_0.5', 'gt_tb_tp_P_3', 'gt_tb_tp_R_3', \ - 'gt_tb_tp_F_3', 'gt_tb_hm_P_0.5', 'gt_tb_hm_R_0.5', 'gt_tb_hm_F_0.5', 'gt_tb_hm_P_3', 'gt_tb_hm_R_3', 'gt_tb_hm_F_3', 'gt_tp_hm_P_0.5', 'gt_tp_hm_R_0.5', 'gt_tp_hm_F_0.5', 'gt_tp_hm_P_3', 'gt_tp_hm_R_3', 'gt_tp_hm_F_3', \ - 'tb_tp_hm_P_0.5', 'tb_tp_hm_R_0.5', 'tb_tp_hm_F_0.5', 'tb_tp_hm_P_3', 'tb_tp_hm_R_3', 'tb_tp_hm_F_3', 'gt_tb_tp_hm_P_0.5', 'gt_tb_tp_hm_R_0.5', 'gt_tb_tp_hm_F_0.5', 'gt_tb_tp_hm_P_3', 'gt_tb_tp_hm_R_3', 'gt_tb_tp_hm_F_3']) + 'gt_hm_P_0.5', 'gt_hm_R_0.5', 'gt_hm_F_0.5', 'gt_hm_P_3', 'gt_hm_R_3', 'gt_hm_F_3', 'tb_tp_P_0.5', 'tb_tp_R_0.5', 'tb_tp_F_0.5', 'tb_tp_P_3', 'tb_tp_R_3', 'tb_tp_F_3', 'hm_tb_P_0.5', 'hm_tb_R_0.5', 'hm_tb_F_0.5', \ + 'hm_tb_P_3', 'hm_tb_R_3', 'hm_tb_F_3', 'hm_tp_P_0.5', 'hm_tp_R_0.5', 'hm_tp_F_0.5', 'hm_tp_P_3', 'hm_tp_R_3', 'hm_tp_F_3', 'gt_tb_tp_P_0.5', 'gt_tb_tp_R_0.5', 'gt_tb_tp_F_0.5', 'gt_tb_tp_P_3', 'gt_tb_tp_R_3', \ + 'gt_tb_tp_F_3', 'gt_hm_tb_P_0.5', 'gt_hm_tb_R_0.5', 'gt_hm_tb_F_0.5', 'gt_hm_tb_P_3', 'gt_hm_tb_R_3', 'gt_hm_tb_F_3', 'gt_hm_tp_P_0.5', 'gt_hm_tp_R_0.5', 'gt_hm_tp_F_0.5', 'gt_hm_tp_P_3', 'gt_hm_tp_R_3', 'gt_hm_tp_F_3', \ + 'hm_tb_tp_P_0.5', 'hm_tb_tp_R_0.5', 'hm_tb_tp_F_0.5', 'hm_tb_tp_P_3', 'hm_tb_tp_R_3', 'hm_tb_tp_F_3', 'gt_hm_tb_tp_P_0.5', 'gt_hm_tb_tp_R_0.5', 'gt_hm_tb_tp_F_0.5', 'gt_hm_tb_tp_P_3', 'gt_hm_tb_tp_R_3', 'gt_hm_tb_tp_F_3']) - def wirteCombinedRes(self, filename, ao_name, gt_hm_res_05, gt_hm_res_3, gt_tb_res_05, gt_tb_res_3, gt_tp_res_05, gt_tp_res_3, hm_tb_res_05, hm_tb_res_3, hm_tp_res_05, hm_tp_res_3, \ - tb_tp_res_05, tb_tp_res_3, gt_hm_tb_res_05, gt_hm_tb_res_3, gt_hm_tp_res_05, gt_hm_tp_res_3, gt_tb_tp_res_05, gt_tb_tp_res_3, hm_tb_tp_res_05, hm_tb_tp_res_3): + def writeCombinedRes(self, filename, ao_name, gt_hm_res_05, gt_hm_res_3, gt_tb_res_05, gt_tb_res_3, gt_tp_res_05, gt_tp_res_3, hm_tb_res_05, hm_tb_res_3, hm_tp_res_05, hm_tp_res_3, \ + tb_tp_res_05, tb_tp_res_3, gt_hm_tb_res_05, gt_hm_tb_res_3, gt_hm_tp_res_05, gt_hm_tp_res_3, gt_tb_tp_res_05, gt_tb_tp_res_3, hm_tb_tp_res_05, hm_tb_tp_res_3, gt_hm_tb_tp_res_05, gt_hm_tb_tp_res_3): '''Write result of single detection for combined features.''' with open(filename, 'a') as f: csvwriter = csv.writer(f, delimiter=',') csvwriter.writerow([ao_name, gt_tb_res_05.P, gt_tb_res_05.R, gt_tb_res_05.F, gt_tb_res_3.P, gt_tb_res_3.R, gt_tb_res_3.F, gt_tp_res_05.P, gt_tp_res_05.R, gt_tp_res_05.F, gt_tp_res_3.P, gt_tp_res_3.R, gt_tp_res_3.F, \ gt_hm_res_05.P, gt_hm_res_05.R, gt_hm_res_05.F, gt_hm_res_3.P, gt_hm_res_3.R, gt_hm_res_3.F, tb_tp_res_05.P, tb_tp_res_05.R, tb_tp_res_05.F, tb_tp_res_3.P, tb_tp_res_3.R, tb_tp_res_3.F, \ - tb_hm_res_05.P, tb_hm_res_05.R, tb_hm_res_05.F, tb_hm_res_3.P, tb_hm_res_3.R, tb_hm_res_3.F, tp_hm_res_05.P, tp_hm_res_05.R, tp_hm_res_05.F, tp_hm_res_3.P, tp_hm_res_3.R, tp_hm_res_3.F, \ - gt_tb_tp_res_05.P, gt_tb_tp_res_05.R, gt_tb_tp_res_05.F, gt_tb_tp_res_3.P, gt_tb_tp_res_3.R, gt_tb_tp_res_3.F, gt_tb_hm_res_05.P, gt_tb_hm_res_05.R, gt_tb_hm_res_05.F, gt_tb_hm_res_3.P, gt_tb_hm_res_3.R, gt_tb_hm_res_3.F, \ - gt_tp_hm_res_05.P, gt_tp_hm_res_05.R, gt_tp_hm_res_05.F, gt_tp_hm_res_3.P, gt_tp_hm_res_3.R, gt_tp_hm_res_3.F, tb_tp_hm_res_05.P, tb_tp_hm_res_05.R, tb_tp_hm_res_05.F, tb_tp_hm_res_3.P, tb_tp_hm_res_3.R, tb_tp_hm_res_3.F, \ - gt_tb_tp_hm_res_05.P, gt_tb_tp_hm_res_05.R, gt_tb_tp_hm_res_05.F, gt_tb_tp_hm_res_3.P, gt_tb_tp_hm_res_3.R, gt_tb_tp_hm_res_3.F]) + hm_tb_res_05.P, hm_tb_res_05.R, hm_tb_res_05.F, hm_tb_res_3.P, hm_tb_res_3.R, hm_tb_res_3.F, hm_tp_res_05.P, hm_tp_res_05.R, hm_tp_res_05.F, hm_tp_res_3.P, hm_tp_res_3.R, hm_tp_res_3.F, \ + gt_tb_tp_res_05.P, gt_tb_tp_res_05.R, gt_tb_tp_res_05.F, gt_tb_tp_res_3.P, gt_tb_tp_res_3.R, gt_tb_tp_res_3.F, gt_hm_tb_res_05.P, gt_hm_tb_res_05.R, gt_hm_tb_res_05.F, gt_hm_tb_res_3.P, gt_hm_tb_res_3.R, gt_hm_tb_res_3.F, \ + gt_hm_tp_res_05.P, gt_hm_tp_res_05.R, gt_hm_tp_res_05.F, gt_hm_tp_res_3.P, gt_hm_tp_res_3.R, gt_hm_tp_res_3.F, hm_tb_tp_res_05.P, hm_tb_tp_res_05.R, hm_tb_tp_res_05.F, hm_tb_tp_res_3.P, hm_tb_tp_res_3.R, hm_tb_tp_res_3.F, \ + gt_hm_tb_tp_res_05.P, gt_hm_tb_tp_res_05.R, gt_hm_tb_tp_res_05.F, gt_hm_tb_tp_res_3.P, gt_hm_tb_tp_res_3.R, gt_hm_tb_tp_res_3.F]) - + def writeMergedHeader(self, filename): + '''Write header of output files merging individual detections.''' + with open(filename, 'a') as f: + csvwriter = csv.writer(f, delimiter=',') + csvwriter.writerow(['audio', 'merged_tp_05', 'merged_fp_05', 'merged_fn_05', 'merged_P_05', 'merged_R_05', 'merged_F_05', 'merged_AD_05', 'merged_DA_05', 'merged_tp_3', \ + 'merged_fp_3', 'merged_fn_3', 'merged_P_3', 'merged_R_3', 'merged_F_3', 'merged_AD_3', 'merged_DA_3']) + + def writeMergedRes(self, filename, ao_name, merged_res_05, merged_res_3): + '''Write results by merging individual detections.''' + with open(filename, 'a') as f: + csvwriter = csv.writer(f, delimiter=',') + csvwriter.writerow([ao_name, merged_res_05.TP, merged_res_05.FP, merged_res_05.FN, merged_res_05.P, merged_res_05.R, merged_res_05.F, merged_res_05.AD, merged_res_05.DA, \ + merged_res_3.TP, merged_res_3.FP, merged_res_3.FN, merged_res_3.P, merged_res_3.R, merged_res_3.F, merged_res_3.AD, merged_res_3.DA]) + + def process(self): '''For the aggregated input features, discard a propertion each time as the pairwise distances within the feature space descending. In the meanwhile evaluate the segmentation result and track the trend of perfomance changing by measuring the feature selection @@ -256,16 +270,17 @@ featureRate = float(self.SampleRate) / self.stepSize audio_files = [x for x in os.listdir(options.GT) if not x.startswith(".") ] - # audio_files = audio_files[:2] + if options.TEST: + audio_files = audio_files[:1] audio_files.sort() audio_list = [] gammatone_feature_list = [i for i in os.listdir(options.GF) if not i.startswith('.')] - gammatone_feature_list = ['contrast4', 'rolloff', 'dct'] + gammatone_feature_list = ['contrast6', 'rolloff4', 'dct'] tempo_feature_list = [i for i in os.listdir(options.TF) if not i.startswith('.')] - tempo_feature_list = ['intensity_bpm', 'loudness_bpm'] - timbre_feature_list = ['mfcc'] - harmonic_feature_list = ['nnls'] + tempo_feature_list = ['ti', 'tir'] + timbre_feature_list = ['mfcc_harmonic'] + harmonic_feature_list = ['chromagram'] gammatone_feature_list = [join(options.GF, f) for f in gammatone_feature_list] timbre_feature_list = [join(options.SF, f) for f in timbre_feature_list] @@ -278,7 +293,6 @@ for audio in audio_files: ao = AudioObj() ao.name = splitext(audio)[0] - print ao.name # annotation_file = join(options.GT, ao.name+'.txt') # iso, salami # ao.gt = np.genfromtxt(annotation_file, usecols=0) # ao.label = np.genfromtxt(annotation_file, usecols=1, dtype=str) @@ -340,13 +354,14 @@ pca = PCA(n_components=5) # Resample and normalise features + step = ao.tempo_features.shape[0] ao.gammatone_features = resample(ao.gammatone_features, step) ao.gammatone_features = normaliseFeature(ao.gammatone_features) ao.timbre_features = resample(ao.timbre_features, step) ao.timbre_features = normaliseFeature(ao.timbre_features) ao.harmonic_features = resample(ao.harmonic_features, step) ao.harmonic_features = normaliseFeature(ao.harmonic_features) - ao.tempo_features = normaliseFeature(ao.harmonic_features) + ao.tempo_features = normaliseFeature(ao.tempo_features) pca.fit(ao.gammatone_features) ao.gammatone_features = pca.transform(ao.gammatone_features) @@ -379,15 +394,19 @@ outfile7 = join(options.OUTPUT, 'combined_sf.csv') outfile8 = join(options.OUTPUT, 'combined_cnmf.csv') - self.wirteIndividualHeader(outfile1) - self.wirteIndividualHeader(outfile2) - self.wirteIndividualHeader(outfile3) - self.wirteIndividualHeader(outfile4) + outfile9 = join(options.OUTPUT, 'individual_merged.csv') + + self.writeIndividualHeader(outfile1) + self.writeIndividualHeader(outfile2) + self.writeIndividualHeader(outfile3) + self.writeIndividualHeader(outfile4) - # self.wirteCombinedlHeader(outfile5) - # self.wirteCombinedlHeader(outfile6) - self.wirteCombinedlHeader(outfile7) - # self.wirteCombinedlHeader(outfile8) + # self.writeCombinedHeader(outfile5) + # self.writeCombinedHeader(outfile6) + self.writeCombinedHeader(outfile7) + self.writeCombinedHeader(outfile8) + + self.writeMergedHeader(outfile9) print 'Segmenting using %s method' %options.BOUNDARY for i,ao in enumerate(audio_list): @@ -453,10 +472,10 @@ timbre_foote_05 = self.pairwiseF(ao.gt, timbre_foote_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) timbre_foote_3 = self.pairwiseF(ao.gt, timbre_foote_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) - self.writeIndividualRes(ao.name, outfile1, gt_novelty_05, gt_novelty_3, harmonic_novelty_05, harmonic_novelty_3, tempo_novelty_05, timbre_novelty_05, timbre_novelty_05, timbre_novelty_3) - self.writeIndividualRes(ao.name, outfile2, gt_cnmf_05, gt_cnmf_3, harmonic_cnmf_05, harmonic_cnmf_3, tempo_cnmf_05, timbre_cnmf_05, timbre_cnmf_05, timbre_cnmf_3) - self.writeIndividualRes(ao.name, outfile3, gt_sf_05, gt_sf_3, harmonic_sf_05, harmonic_sf_3, tempo_sf_05, timbre_sf_05, timbre_sf_05, timbre_sf_3) - self.writeIndividualRes(ao.name, outfile4, gt_foote_05, gt_foote_3, harmonic_foote_05, harmonic_foote_3, tempo_foote_05, timbre_foote_05, timbre_foote_05, timbre_foote_3) + self.writeIndividualRes(outfile1, ao.name, gt_novelty_05, gt_novelty_3, harmonic_novelty_05, harmonic_novelty_3, tempo_novelty_05, timbre_novelty_05, timbre_novelty_05, timbre_novelty_3) + self.writeIndividualRes(outfile2, ao.name, gt_cnmf_05, gt_cnmf_3, harmonic_cnmf_05, harmonic_cnmf_3, tempo_cnmf_05, timbre_cnmf_05, timbre_cnmf_05, timbre_cnmf_3) + self.writeIndividualRes(outfile3, ao.name, gt_sf_05, gt_sf_3, harmonic_sf_05, harmonic_sf_3, tempo_sf_05, timbre_sf_05, timbre_sf_05, timbre_sf_3) + self.writeIndividualRes(outfile4, ao.name, gt_foote_05, gt_foote_3, harmonic_foote_05, harmonic_foote_3, tempo_foote_05, timbre_foote_05, timbre_foote_05, timbre_foote_3) ############################################################################################################################################ @@ -469,11 +488,13 @@ hm_tb = np.hstack([ao.harmonic_features, ao.timbre_features]) hm_tp = np.hstack([ao.harmonic_features, ao.tempo_features]) tb_tp = np.hstack([ao.timbre_features, ao.tempo_features]) + gt_hm_tb = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.timbre_features]) gt_hm_tp = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.tempo_features]) gt_tb_tp = np.hstack([ao.gammatone_features, ao.timbre_features, ao.tempo_features]) hm_tb_tp = np.hstack([ao.harmonic_features, ao.timbre_features, ao.tempo_features]) gt_hm_tb_tp = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.timbre_features, ao.tempo_features]) + # Evaluting and writing results. gt_hm_sf_idxs = sf_S.segmentation(gt_hm) gt_tb_sf_idxs = sf_S.segmentation(gt_tb) gt_tp_sf_idxs = sf_S.segmentation(gt_tp) @@ -486,37 +507,102 @@ hm_tb_tp_sf_idxs = sf_S.segmentation(hm_tb_tp) gt_hm_tb_tp_sf_idxs = sf_S.segmentation(gt_hm_tb_tp) - gt_hm_05 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=0.5, combine=1.0) - gt_tb_05 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=0.5, combine=1.0) - gt_tp_05 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=0.5, combine=1.0) - hm_tb_05 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=0.5, combine=1.0) - hm_tp_05 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=0.5, combine=1.0) - tb_tp_05 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=0.5, combine=1.0) - gt_hm_tb_05 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=0.5, combine=1.0) - gt_hm_tp_05 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=0.5, combine=1.0) - gt_tb_tp_05 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=0.5, combine=1.0) - hm_tb_tp_05 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0) - gt_hm_tb_tp_05 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0) + gt_hm_cnmf_idxs = cnmf_S.segmentation(gt_hm, rank=4, R=R, h=h, niter=300) + gt_tb_cnmf_idxs = cnmf_S.segmentation(gt_tb, rank=4, R=R, h=h, niter=300) + gt_tp_cnmf_idxs = cnmf_S.segmentation(gt_tp, rank=4, R=R, h=h, niter=300) + hm_tb_cnmf_idxs = cnmf_S.segmentation(hm_tb, rank=4, R=R, h=h, niter=300) + hm_tp_cnmf_idxs = cnmf_S.segmentation(hm_tp, rank=4, R=R, h=h, niter=300) + tb_tp_cnmf_idxs = cnmf_S.segmentation(tb_tp, rank=4, R=R, h=h, niter=300) + gt_hm_tb_cnmf_idxs = cnmf_S.segmentation(gt_hm_tb, rank=6, R=R, h=h, niter=300) + gt_hm_tp_cnmf_idxs = cnmf_S.segmentation(gt_hm_tp, rank=6, R=R, h=h, niter=300) + gt_tb_tp_cnmf_idxs = cnmf_S.segmentation(gt_tb_tp, rank=6, R=R, h=h, niter=300) + hm_tb_tp_cnmf_idxs = cnmf_S.segmentation(hm_tb_tp, rank=6, R=R, h=h, niter=300) + gt_hm_tb_tp_cnmf_idxs = cnmf_S.segmentation(gt_hm_tb_tp, rank=8, R=R, h=h, niter=300) - gt_hm_3 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=3, combine=1.0) - gt_tb_3 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=3, combine=1.0) - gt_tp_3 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=3, combine=1.0) - hm_tb_3 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=3, combine=1.0) - hm_tp_3 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=3, combine=1.0) - tb_tp_3 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=3, combine=1.0) - gt_hm_tb_3 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=3, combine=1.0) - gt_hm_tp_3 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=3, combine=1.0) - gt_tb_tp_3 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=3, combine=1.0) - hm_tb_tp_3 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=3, combine=1.0) - gt_hm_tb_tp_3 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=3, combine=1.0) + gt_hm_sf_05 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_sf_05 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tp_sf_05 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_sf_05 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tp_sf_05 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + tb_tp_sf_05 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_sf_05 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tp_sf_05 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_tp_sf_05 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_tp_sf_05 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_tp_sf_05 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + + gt_hm_sf_3 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_sf_3 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tp_sf_3 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_sf_3 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tp_sf_3 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + tb_tp_sf_3 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_sf_3 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tp_sf_3 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_tp_sf_3 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_tp_sf_3 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_tp_sf_3 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + + gt_hm_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_cnmf_05 = self.pairwiseF(ao.gt, gt_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_cnmf_05 = self.pairwiseF(ao.gt, hm_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tp_cnmf_05 = self.pairwiseF(ao.gt, hm_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + tb_tp_cnmf_05 = self.pairwiseF(ao.gt, tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, hm_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + + gt_hm_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_cnmf_3 = self.pairwiseF(ao.gt, gt_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_cnmf_3 = self.pairwiseF(ao.gt, hm_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tp_cnmf_3 = self.pairwiseF(ao.gt, hm_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + tb_tp_cnmf_3 = self.pairwiseF(ao.gt, tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + hm_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, hm_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + gt_hm_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) - self.writeCombinedRes(ao.name, outfile7, gt_hm_05, gt_hm_3, gt_tb_05, gt_tb_3, gt_tp_05, gt_tp_3, hm_tb_05, hm_tb_3, hm_tp_05, hm_tp_3, tb_tp_05, tb_tp_3\ - gt_hm_tb_05, gt_hm_tb_3, gt_hm_tp_05, gt_hm_tp_3, gt_tb_tp_05, gt_tb_tp_3, hm_tb_tp_05, hm_tb_tp_3, gt_hm_tb_tp_05, gt_hm_tb_tp_3) + self.writeCombinedRes(outfile7, ao.name, gt_hm_sf_05, gt_hm_sf_3, gt_tb_sf_05, gt_tb_sf_3, gt_tp_sf_05, gt_tp_sf_3, hm_tb_sf_05, hm_tb_sf_3, hm_tp_sf_05, hm_tp_sf_3, tb_tp_sf_05, tb_tp_sf_3,\ + gt_hm_tb_sf_05, gt_hm_tb_sf_3, gt_hm_tp_sf_05, gt_hm_tp_sf_3, gt_tb_tp_sf_05, gt_tb_tp_sf_3, hm_tb_tp_sf_05, hm_tb_tp_sf_3, gt_hm_tb_tp_sf_05, gt_hm_tb_tp_sf_3) + + self.writeCombinedRes(outfile8, ao.name, gt_hm_cnmf_05, gt_hm_cnmf_3, gt_tb_cnmf_05, gt_tb_cnmf_3, gt_tp_cnmf_05, gt_tp_cnmf_3, hm_tb_cnmf_05, hm_tb_cnmf_3, hm_tp_cnmf_05, hm_tp_cnmf_3, tb_tp_cnmf_05, tb_tp_cnmf_3,\ + gt_hm_tb_cnmf_05, gt_hm_tb_cnmf_3, gt_hm_tp_cnmf_05, gt_hm_tp_cnmf_3, gt_tb_tp_cnmf_05, gt_tb_tp_cnmf_3, hm_tb_tp_cnmf_05, hm_tb_tp_cnmf_3, gt_hm_tb_tp_cnmf_05, gt_hm_tb_tp_cnmf_3) ############################################################################################################################################ # Experiment 3: Pruning boundaries detected by individual boundary algorithms. - + # Use different boundary methods for different features + gammatone_idxs, harmonic_idxs, timbre_idxs, tempo_idxs = gammatone_sf_idxs, harmonic_sf_idxs, timbre_sf_idxs, tempo_sf_idxs + bound_candidates = list(gammatone_idxs) + list(harmonic_idxs) + list(timbre_idxs) + list(tempo_idxs) + bound_candidates.sort() + + nBounds = len(bound_candidates) + final_idxs = [] + idx = 0 + tol = 10 # tolerance window of merging boundary scores + while idx < nBounds: + temp = [bound_candidates[idx]] + pos = [idx] + idx += 1 + while (idx + tol < nBounds and np.max(bound_candidates[idx: idx+tol]) > 0): + temp += [bound_candidates[idx+delta] for delta in xrange(tol) if (bound_candidates[idx]+delta in bound_candidates)] + pos += [idx+delta for delta in xrange(tol) if (bound_candidates[idx]+delta in bound_candidates)] + idx += tol + if len(temp) == 1: + final_idxs.append(temp[0]) + else: + final_idxs.append(int(np.rint(np.mean(temp)))) + + merged_05 = self.pairwiseF(ao.gt, final_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps) + merged_3 = self.pairwiseF(ao.gt, final_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps) + + self.writeMergedRes(outfile9, ao.name, merged_05, merged_3) + # if options.BOUNDARY == 'novelty': # gammatone_novelty, smoothed_gammatone_novelty, gammatone_bound_idxs = novelty_S.process(ao.gammatone_ssm, self.kernel_size, peak_picker) # timbre_novelty, smoothed_timbre_novelty, timbre_bound_idxs = novelty_S.process(ao.timbre_ssm, self.kernel_size, peak_picker)
--- a/cnmf.py Tue Apr 07 17:58:37 2015 +0100 +++ b/cnmf.py Wed Apr 08 11:47:47 2015 +0100 @@ -84,7 +84,7 @@ G[:, :] = 0 G[max_idx] = idx + 1 G = np.sum(G, axis=1) - G = utils.median_filter(G[:, np.newaxis], R) + G = SegUtil.median_filter(G[:, np.newaxis], R) return G.flatten()
--- a/utils/SegUtil.py Tue Apr 07 17:58:37 2015 +0100 +++ b/utils/SegUtil.py Wed Apr 08 11:47:47 2015 +0100 @@ -22,6 +22,7 @@ import pylab as plt from scipy.spatial.distance import squareform, pdist from scipy.ndimage.filters import * +from sklearn.metrics.pairwise import pairwise_distances def lognormalize_chroma(C):