comparison toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo8classification.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function demo8classification
+% To get familiar with different approaches of classification using 
+% MIRtoolbox, and to assess their performances. 
+
+% Part 1. The aim of this experiment is to categorize a set of very short 
+% musical excerpts according to their genres, through a supervised learning.
+
+% 1.3. Select the training set for current directory.
+try
+    cd train_set
+catch
+    error('Please change current directory to ''MIRtoolboxDemos'' directory')
+end
+
+% Load all the files of the folder into one audio structure (called for 
+% instance training), and associate for each folder a label defined by the
+% first two letters of the respective file name.
+train = miraudio('Folder','Label',1:2);
+cd ..
+
+% In the same way, select the testing set for current directory, and load
+% all the files including their labels:
+cd test_set
+test = miraudio('Folder','Label',1:2);
+cd ..
+
+% 1.4. Compute the mel-frequency cepstrum coefficient for each different 
+% audio file of both sets:
+mfcc_train = mirmfcc(train);
+mfcc_test = mirmfcc(test);
+
+% 1.5.  Estimate the label (i.e., genre) of each file from the testing set,
+% based on a prior learning using the training set. Use for this purpose
+% the classify function.
+help mirclassify
+
+% Let's first try a classification based of mfcc, for instance, using the
+% minimum distance strategy:
+mirclassify(test,mfcc_test,train,mfcc_train)
+
+% The results indicates the outcomes and the total correct classification
+% rate (CCR).
+
+% 1.6. Let's try a k-nearest-neighbour strategy. For instance, for k = 5:
+mirclassify(test,mfcc_test,train,mfcc_train,5)
+
+% 1.7. Use a Gaussian mixture modelling with one gaussian per class:
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1)
+
+% try also with three Gaussians per class.
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3)
+
+% As this strategy is stochastic, the results vary for every trial.
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1)
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1)
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3)
+mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3)
+
+% 1.8. Carry out the classification using other features such as spectral
+% centroid:
+spectrum_train = mirspectrum(train);
+spectrum_test = mirspectrum(test);
+centroid_train = mircentroid(spectrum_train);
+centroid_test = mircentroid(spectrum_test);
+mirclassify(test,centroid_test,train,centroid_train,'GMM',1)
+mirclassify(test,centroid_test,train,centroid_train,'GMM',1)
+mirclassify(test,centroid_test,train,centroid_train,'GMM',3)
+mirclassify(test,centroid_test,train,centroid_train,'GMM',3)
+
+% try also spectral entropy and spectral irregularity. 
+entropy_train = mirentropy(spectrum_train);
+entropy_test = mirentropy(spectrum_test);
+mirclassify(test,entropy_test,train,entropy_train,'GMM',1)
+mirclassify(test,entropy_test,train,entropy_train,'GMM',1)
+mirclassify(test,entropy_test,train,entropy_train,'GMM',3)
+mirclassify(test,entropy_test,train,entropy_train,'GMM',3)
+
+irregularity_train = mirregularity(spectrum_train,'Contrast',.1);
+irregularity_test = mirregularity(spectrum_test,'Contrast',.1);
+mirclassify(test,irregularity_test,train,irregularity_train,'GMM',1)
+mirclassify(test,irregularity_test,train,irregularity_train,'GMM',1)
+mirclassify(test,irregularity_test,train,irregularity_train,'GMM',3)
+mirclassify(test,irregularity_test,train,irregularity_train,'GMM',3)
+
+% Try classification based on a set of features such as:
+mirclassify(test,{entropy_test,centroid_test},...
+         train,{entropy_train,centroid_train},'GMM',1)
+mirclassify(test,{entropy_test,centroid_test},...
+         train,{entropy_train,centroid_train},'GMM',1)
+mirclassify(test,{entropy_test,centroid_test},...
+         train,{entropy_train,centroid_train},'GMM',3)
+mirclassify(test,{entropy_test,centroid_test},...
+         train,{entropy_train,centroid_train},'GMM',3)
+
+% 1.9. By varying the features used for classification, the strategies and
+% their parameters, try to find an optimal strategy that give best correct
+% classification rate.
+bright_train = mirbrightness(spectrum_train);
+bright_test = mirbrightness(spectrum_test);
+rolloff_train = mirbrightness(spectrum_train);
+rolloff_test = mirbrightness(spectrum_test);
+spread_train = mirspread(spectrum_train);
+spread_test = mirspread(spectrum_test);
+mirclassify(test,{bright_test,rolloff_test,spread_test},...
+         train,{bright_train,rolloff_train,spread_train},'GMM',3)
+skew_train = mirskewness(spectrum_train);
+skew_test = mirskewness(spectrum_test);
+kurtosis_train = mirkurtosis(spectrum_train);
+kurtosis_test = mirkurtosis(spectrum_test);
+flat_train = mirflatness(spectrum_train);
+flat_test = mirflatness(spectrum_test);
+mirclassify(test,{skew_test,kurtosis_test,flat_test},...
+         train,{skew_train,kurtosis_train,flat_train},'GMM',3)
+for i = 1:3
+     mirclassify(test,{mfcc_test,centroid_test,skew_test,kurtosis_test,...
+               flat_test,entropy_test,irregularity_test,...
+               bright_test,rolloff_test,spread_test},...
+              train,{mfcc_train,centroid_train,skew_train,kurtosis_train,...
+                    flat_train,entropy_train,irregularity_train,...
+                    bright_train,rolloff_train,spread_train},'GMM',3)
+end
+
+% You can also try to change the size of the training and testing sets (by
+% simply interverting them for instance). 
+for i = 1:3
+    mirclassify(train,{mfcc_train,centroid_train,skew_train,kurtosis_train,...
+                flat_train,entropy_train,irregularity_train,...
+                bright_train,rolloff_train,spread_train},...
+             test,{mfcc_test,centroid_test,skew_test,kurtosis_test,...
+                   flat_test,entropy_test,irregularity_test,...
+                   bright_test,rolloff_test,spread_test},'GMM',3)
+end
+
+%%
+% Part 2. In this second experiment, we will try to cluster the segments of
+% an audio file according to their mutual similarity.
+
+% 2.1.  To simplify the computation, downsample
+% the audio file to 11025 Hz.
+a = miraudio('czardas','Sampling',11025);
+
+% 2.2. Decompose the file into successive frames of 2 seconds with half-
+% overlapping.
+f = mirframe(a,2,.1);
+
+% 2.3. Segment the file based on the novelty of the key strengths.
+n = mirnovelty(mirkeystrength(f),'KernelSize',5)
+p = mirpeaks(n)
+s = mirsegment(a,p)
+
+% 2.4. Compute the key strengths of each segment.
+ks = mirkeystrength(s)
+
+% 2.5. Cluster the segments according to their key strengths.
+help mircluster
+mircluster(s,ks)
+
+% The k means algorithm used in the clustering is stochastic, and its
+% results may vary at each run. By default, the algorithm is run 5 times 
+% and the best result is selected. Try the analysis with a higher number of
+% runs:
+mircluster(s,ks,'Runs',10)