Dawn@4: function [] = kmeans_MFCC_Singing( inputFileName, outputFileName )
Dawn@4: 
Dawn@4: cd 'C:\Users\dawn\Dropbox\TestResults'
Dawn@4: inputFileName
Dawn@4: 
Dawn@4: DEBUG = 1;
Dawn@4: % output results file name
Dawn@4: masterFileOutputID = fopen( outputFileName, 'a' );   %fopen( 'kmeans_Singing_MFCC.txt', 'a' );
Dawn@4: % % input results file name
Dawn@4: % inputFileName = 'singingMFCCStats_VoicedAndUnvoiced.txt';
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, '\n RESULTS FILE NAME: %s\n', inputFileName);
Dawn@4: inputFileID = fopen( inputFileName );
Dawn@4: 
Dawn@4: % noOfArguments = length(varargin);
Dawn@4: % 
Dawn@4: % outputFileName = 'individualResults/kmeans_Results_';
Dawn@4: % resultsFileName = 'kmeans_Results_';
Dawn@4: titleName = '';
Dawn@4: % for i=1 : noOfArguments
Dawn@4: %     titleName = [ titleName varargin{i} '_'];
Dawn@4: %     fprintf( masterFileOutputID, '%s_', varargin{i} );
Dawn@4: % end
Dawn@4: % 
Dawn@4: % outputFileName = [ outputFileName titleName ];
Dawn@4: % resultsFileName = [ resultsFileName titleName ];
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, '\t' );
Dawn@4: 
Dawn@4: % outputFileName = [ outputFileName '.txt'];
Dawn@4: % resultsFileName = [ resultsFileName '.txt'];
Dawn@4: 
Dawn@4: % fileOutputID = fopen( outputFileName, 'w' );
Dawn@4: % fileKMeansOutputID = fopen( resultsFileName, 'w' );
Dawn@4: 
Dawn@4: % -------------------- get the data from the results file ---------------
Dawn@4: lineCount = 0;
Dawn@4: fileCount = 0;
Dawn@4: data = [];
Dawn@4: while( ~(feof(inputFileID)) )
Dawn@4:     
Dawn@4:     outputValues = [];
Dawn@4:     thestr = fgetl(inputFileID);
Dawn@4:     fileCount = fileCount + 1;
Dawn@4: 
Dawn@4:     % determine whether we have a positive or negative sample
Dawn@4:     sampleEmotion( fileCount ) = 'U';
Dawn@4:     if( ~(isempty(strfind(thestr,'pos'))))
Dawn@4:         % sample is positive
Dawn@4:         sampleEmotion( fileCount ) = 'P';
Dawn@4:     elseif( ~(isempty(strfind(thestr,'neg'))))
Dawn@4:         % sample is negative
Dawn@4:         sampleEmotion( fileCount ) = 'N';
Dawn@4:     else
Dawn@4:         disp('EEEK!');
Dawn@4:         pause;
Dawn@4:     end
Dawn@4: 
Dawn@4:     % determine whether we have a male, female or trans sample
Dawn@4:     gender( fileCount ) = '?';
Dawn@4:     if( ~(isempty(strfind(thestr,'fem'))))
Dawn@4:         % gender is female
Dawn@4:         gender( fileCount ) = 'F';
Dawn@4:     elseif( ~(isempty(strfind(thestr,'male'))))
Dawn@4:         % gender is male
Dawn@4:         gender( fileCount ) = 'M';
Dawn@4:     elseif( ~(isempty(strfind(thestr,'trans'))))
Dawn@4:         % gender is trans
Dawn@4:         gender( fileCount ) = 'T';
Dawn@4:     else
Dawn@4:         disp('EEEK!');
Dawn@4:         pause;
Dawn@4:     end
Dawn@4: 
Dawn@4:     %how many values are in the string?
Dawn@4:     spaces = strfind( thestr, ' ' );
Dawn@4:     numberstr = thestr( spaces(1) : end ); % chop off the file name  
Dawn@4:     vars = sscanf( numberstr, '%f', inf );
Dawn@4:     data( fileCount, : ) = vars;
Dawn@4:         
Dawn@4:     lineCount = lineCount + 1;
Dawn@4:     
Dawn@4: end
Dawn@4: fclose(inputFileID);
Dawn@4: 
Dawn@4: % try with and without different metrics classes
Dawn@4: % data is a 162 variable array of MFCC stats
Dawn@4: % first there are 6 metrics for each of the 13 frequency bands (mean, median, var, min, max, range) 
Dawn@4: % then there are 6 metrics for the 1st derivative of the 13 frequency bands (mean, median, var, min, max, range) 
Dawn@4: % then there are 6 metrics for the mean of all frequency bands
Dawn@4: 
Dawn@4: %  dataOptions = (1:1:(13*12)+6); % fprintf( masterFileOutputID, '\n try with all \n');
Dawn@4: % dataOptions = (1:1:13*6);  fprintf( masterFileOutputID, '\n try with just the ordinary 13 frequency bands \n');
Dawn@4: % dataOptions = (1:1:13*6);  fprintf( masterFileOutputID, '\n try with just the ordinary 13 frequency bands and without the median or range \n');
Dawn@4: 
Dawn@4: % ------------  apply the k-means classifier  ------------------------
Dawn@4: 
Dawn@4: noOfClusters = 2; % we are only trying to identify positive and negative emotions
Dawn@4: 
Dawn@4: 
Dawn@4: [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
Dawn@4:     'start', 'sample', 'Distance', 'cityblock');
Dawn@4: 
Dawn@4: %display results grouped by emotion
Dawn@4: fprintf( masterFileOutputID, '\n Emotion grouping \n');
Dawn@4: fprintf( masterFileOutputID, 'cityblock \n');
Dawn@4: [ groupStats, groupNames ] = processKMeansResults( 'cityblock', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
Dawn@4: [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
Dawn@4: % if(DEBUG == 1)
Dawn@4: %     disp('press space');
Dawn@4: %     pause;
Dawn@4: % end
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, 'sqEuclidean \n');
Dawn@4: [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
Dawn@4:     'start', 'sample', 'Distance', 'sqEuclidean');
Dawn@4: 
Dawn@4: [ groupStats, groupNames ] = processKMeansResults( 'sqEuclidean', idx, sampleEmotion, masterFileOutputID, titleName, DEBUG );
Dawn@4: [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
Dawn@4: 
Dawn@4: % if(DEBUG == 1)
Dawn@4: %     disp('press space');
Dawn@4: %     pause;
Dawn@4: % end
Dawn@4: 
Dawn@4: % display results grouped by gender
Dawn@4: fprintf( masterFileOutputID, '\n Gender grouping \n');
Dawn@4: noOfClusters = 3;
Dawn@4: 
Dawn@4: [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
Dawn@4:     'start', 'sample', 'Distance', 'cityblock');
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, 'cityblock \n');
Dawn@4: [ groupStats, groupNames ] = processKMeansResults( 'cityblock', idx, gender, masterFileOutputID, titleName, DEBUG );
Dawn@4: [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
Dawn@4: 
Dawn@4: [idx ctrs]=kmeans( data, noOfClusters, 'Replicates',100,...
Dawn@4:     'start', 'sample', 'Distance', 'sqEuclidean');
Dawn@4: 
Dawn@4: fprintf( masterFileOutputID, 'sqEuclidean \n');
Dawn@4: [ groupStats, groupNames ] = processKMeansResults( 'sqEuclidean', idx, gender, masterFileOutputID, titleName, DEBUG );
Dawn@4: [ confusionMatrix ] = getConfusionMatrix( groupStats, groupNames, masterFileOutputID );
Dawn@4: 
Dawn@4: 
Dawn@4: % fprintf( fileOutputID, '\n' );
Dawn@4: % fclose( fileOutputID );
Dawn@4: % fprintf( fileKMeansOutputID, '\n' );
Dawn@4: % fclose( fileKMeansOutputID );
Dawn@4: fprintf( masterFileOutputID, '\n' );
Dawn@4: fclose( masterFileOutputID );
Dawn@4: 
Dawn@4: end
Dawn@4: 
Dawn@4: