Mercurial > hg > emotion-detection-top-level
diff Code/Classifiers/SVM_Formants_Singing.m @ 4:92ca03a8fa99 tip
Update to ICASSP 2013 benchmark
| author | Dawn Black |
|---|---|
| date | Wed, 13 Feb 2013 11:02:39 +0000 |
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| children |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Code/Classifiers/SVM_Formants_Singing.m Wed Feb 13 11:02:39 2013 +0000 @@ -0,0 +1,540 @@ +function [] = SVM_Formants_Singing( varargin ) + +cd 'C:\Users\dawn\Dropbox\TestResults' + +DEBUG = 0; +% output results file name +masterFileOutputID = fopen( 'SVM_paper_SingingFormantsStats.txt', 'a' ); +% input results file name +inputFileName = 'paper_singingFormantsStats.txt'; + +% This function allows the user to stipulate which Singing voice LLD's they +% wish to forward to a k-means classifier and produces a file of +% performance characteristics. Input arguments stipulate the LLD's and +% there is a choice of:- +% +% ---- PRAAT FORMANT MEASUREMENTS ---- +% '_Formant_Burg' +% '_Formant_all' +% '_Formant_robust' +% +% A text file entitled kmeans_Singing_LLD1name_LLD2name_ ... LLDNname.txt +% is produced that contains the results of the k-mean classification for +% the LLD's specified and named in the result document title. + +fprintf( masterFileOutputID, '\n RESULTS FILE NAME: %s\n', inputFileName); +inputFileID = fopen( inputFileName ); + +% get the column numbers of the results that we want to classify + +% COLUMN NUMBER : METRIC +% +% ------------- BURG FORMANTS --------------- +% 11 : Number of BURG formants listed = nBF +% +% THERE ARE CURRENTLY 24 MEASUREMENTS TAKEN FOR EACH FORMANT +nMetrics = 24; +% +% 12 : mean frequency of the first BURG formant +% 13 : variance of the first BURG formant +% 14 : minimum frequency of the first BURG formant +% 15 : maximum frequency of the first BURG formant +% 16 : mean Frequency Derivative of the first BURG formant +% 17 : varience of the Frequency Derivative of the first BURG formant +% 18 : min of the Frequency Derivative of the first BURG formant +% 19 : max of the Frequency Derivative of the first BURG formant +% 20 : mean of the Frequency 2nd Derivative of the first BURG formant +% 21 : varience of the Frequency 2nd Derivative of the first BURG formant +% 22 : min of the Frequency 2nd Derivative of the first BURG formant +% 23 : max of the Frequency 2nd Derivative of the first BURG formant +% 24 : mean of the Bandwidth of the first BURG formant +% 25 : varience of the Bandwidth of the first BURG formant +% 26 : min of the Bandwidth of the first BURG formant +% 27 : max of the Bandwidth of the first BURG formant +% 28 : mean of the Bandwidth Derivative of the first BURG formant +% 29 : varience of the Bandwidth Derivative of the first BURG formant +% 30 : min of the Bandwidth Derivative of the first BURG formant +% 31 : max of the Bandwidth Derivative of the first BURG formant +% 32 : mean of the Bandwidth 2nd Derivative of the first BURG formant +% 33 : var of the Bandwidth 2nd Derivative of the first BURG formant +% 34 : min of the Bandwidth 2nd Derivative of the first BURG formant +% 35 : max of the Bandwidth 2nd Derivative of the first BURG formant +% +% ....... there are nMetrics for each formant in nBF formants, so cycle +% through until the last is reached ...... +% +% 36 + ((nBF-1)*nMetrics) : mean frequency of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 1 : variance of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 2 : minimum frequency of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 3 : maximum frequency of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 4 : mean Frequency Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 5 : varience of the Frequency Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 6 : min of the Frequency Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 7 : max of the Frequency Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 8 : mean of the Frequency 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 9 : varience of the Frequency 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 10 : min of the Frequency 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 11 : max of the Frequency 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 12 : mean of the Bandwidth of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 13 : varience of the Bandwidth of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 14 : min of the Bandwidth of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 15 : max of the Bandwidth of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 16 : mean of the Bandwidth Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 17 : variece of the Bandwidth Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 18 : min of the Bandwidth Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 19 : max of the Bandwidth Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 20 : mean of the Bandwidth 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 21 : var of the Bandwidth 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 22 : min of the Bandwidth 2nd Derivative of the nBF BURG formant +% 36 + ((nBF-1)*nMetrics) + 23 : max of the Bandwidth 2nd Derivative of the nBF BURG formant +% +% FOR THE MEAN OF ALL BURG FORMANTS +% 36 + (nBF*nMetrics) : mean of all formants Frequency +% 36 + (nBF*nMetrics) + 1 : varience of the mean of all formants Frequency +% 36 + (nBF*nMetrics) + 2 : minimum of the mean of all formants Frequency +% 36 + (nBF*nMetrics) + 3 : maximum of the mean of all formants Frequency +% 36 + (nBF*nMetrics) + 4 : mean of all formants mean Frequency Derivative +% 36 + (nBF*nMetrics) + 5 : mean of all formants varience Frequency Derivative +% 36 + (nBF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative +% 36 + (nBF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative +% 36 + (nBF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative +% 36 + (nBF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative +% 36 + (nBF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative +% 36 + (nBF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative +% +% ------------- ALL FORMANTS --------------- +% +% 36 + (nBF*nMetrics) + 12 : Number of ALL formants listed = nAF +% +% startOfALLMeasurements = 36 + (nBF*nMetrics) + 13; +% +% startOfALLMeasurements : mean frequency of the first ALL formant +% startOfALLMeasurements + 1 : variance of the first ALL formant +% startOfALLMeasurements + 2 : minimum frequency of the first ALL formant +% startOfALLMeasurements + 3 : maximum frequency of the first ALL formant +% startOfALLMeasurements + 4 : mean Frequency Derivative of the first ALL formant +% startOfALLMeasurements + 5 : varience of the Frequency Derivative of the first ALL formant +% startOfALLMeasurements + 6 : min of the Frequency Derivative of the first ALL formant +% startOfALLMeasurements + 7 : max of the Frequency Derivative of the first ALL formant +% startOfALLMeasurements + 8 : mean of the Frequency 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 9 : varience of the Frequency 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 10 : min of the Frequency 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 11 : max of the Frequency 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 12 : mean of the Bandwidth of the first ALL formant +% startOfALLMeasurements + 13 : varience of the Bandwidth of the first ALL formant +% startOfALLMeasurements + 14 : min of the Bandwidth of the first ALL formant +% startOfALLMeasurements + 15 : max of the Bandwidth of the first ALL formant +% startOfALLMeasurements + 16 : mean of the Bandwidth Derivative of the first ALL formant +% startOfALLMeasurements + 17 : varience of the Bandwidth Derivative of the first ALL formant +% startOfALLMeasurements + 18 : min of the Bandwidth Derivative of the first ALL formant +% startOfALLMeasurements + 19 : max of the Bandwidth Derivative of the first ALL formant +% startOfALLMeasurements + 20 : mean of the Bandwidth 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 21 : var of the Bandwidth 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 22 : min of the Bandwidth 2nd Derivative of the first ALL formant +% startOfALLMeasurements + 23 : max of the Bandwidth 2nd Derivative of the first ALL formant +% +% ....... there are nMetrics for each formant in nAF formants, so cycle +% through until the last is reached ...... +% +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean frequency of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : variance of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : minimum frequency of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : maximum frequency of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean Frequency Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Frequency Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Frequency Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Frequency Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Frequency 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Frequency 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Frequency 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Frequency 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : varience of the Bandwidth of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : variece of the Bandwidth Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : mean of the Bandwidth 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : var of the Bandwidth 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : min of the Bandwidth 2nd Derivative of the nAF ALL formant +% startOfALLMeasurements + ((nAF-1)*nMetrics) : max of the Bandwidth 2nd Derivative of the nAF ALL formant +% +% FOR THE MEAN OF ALL ALL FORMANTS +% startOfALLMeasurements + (nAF*nMetrics) : mean of all formants Frequency +% startOfALLMeasurements + (nAF*nMetrics) + 1 : varience of the mean of all formants Frequency +% startOfALLMeasurements + (nAF*nMetrics) + 2 : minimum of the mean of all formants Frequency +% startOfALLMeasurements + (nAF*nMetrics) + 3 : maximum of the mean of all formants Frequency +% startOfALLMeasurements + (nAF*nMetrics) + 4 : mean of all formants mean Frequency Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 5 : mean of all formants varience Frequency Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative +% startOfALLMeasurements + (nAF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative +% +% ------------- ROBUST FORMANTS --------------- +% +% startOfALLMeasurements + (nAF*nMetrics) + 12 : Number of ROBUST formants listed = nRF +% +% startOfROBUSTMeasurements = startOfALLMeasurements + (nAF*nMetrics) + 13; +% +% startOfROBUSTMeasurements : mean frequency of the first ROBUST formant +% startOfROBUSTMeasurements + 1 : variance of the first ROBUST formant +% startOfROBUSTMeasurements + 2 : minimum frequency of the first ROBUST formant +% startOfROBUSTMeasurements + 3 : maximum frequency of the first ROBUST formant +% startOfROBUSTMeasurements + 4 : mean Frequency Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 5 : varience of the Frequency Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 6 : min of the Frequency Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 7 : max of the Frequency Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 8 : mean of the Frequency 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 9 : varience of the Frequency 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 10 : min of the Frequency 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 11 : max of the Frequency 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 12 : mean of the Bandwidth of the first ROBUST formant +% startOfROBUSTMeasurements + 13 : varience of the Bandwidth of the first ROBUST formant +% startOfROBUSTMeasurements + 14 : min of the Bandwidth of the first ROBUST formant +% startOfROBUSTMeasurements + 15 : max of the Bandwidth of the first ROBUST formant +% startOfROBUSTMeasurements + 16 : mean of the Bandwidth Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 17 : varience of the Bandwidth Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 18 : min of the Bandwidth Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 19 : max of the Bandwidth Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 20 : mean of the Bandwidth 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 21 : var of the Bandwidth 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 22 : min of the Bandwidth 2nd Derivative of the first ROBUST formant +% startOfROBUSTMeasurements + 23 : max of the Bandwidth 2nd Derivative of the first ROBUST formant +% +% ....... there are nMetrics for each formant in nRF formants, so cycle +% through until the last is reached ...... +% +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean frequency of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : variance of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : minimum frequency of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : maximum frequency of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean Frequency Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Frequency Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Frequency Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Frequency Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Frequency 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Frequency 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Frequency 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Frequency 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : varience of the Bandwidth of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : variece of the Bandwidth Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : mean of the Bandwidth 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : var of the Bandwidth 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : min of the Bandwidth 2nd Derivative of the nRF ROBUST formant +% startOfROBUSTMeasurements + ((nRF-1)*nMetrics) : max of the Bandwidth 2nd Derivative of the nRF ROBUST formant +% +% FOR THE MEAN OF ALL ROBUST FORMANTS +% startOfROBUSTMeasurements + (nRF*nMetrics) : mean of all formants Frequency +% startOfROBUSTMeasurements + (nRF*nMetrics) + 1 : varience of the mean of all formants Frequency +% startOfROBUSTMeasurements + (nRF*nMetrics) + 2 : minimum of the mean of all formants Frequency +% startOfROBUSTMeasurements + (nRF*nMetrics) + 3 : maximum of the mean of all formants Frequency +% startOfROBUSTMeasurements + (nRF*nMetrics) + 4 : mean of all formants mean Frequency Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 5 : mean of all formants varience Frequency Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 6 : min of the mean of all formants Frequency Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 7 : max of the mean of all formants Frequency Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 8 : mean of the mean of all formants Frequency 2nd Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 9 : varience of the mean of all formants Frequency 2nd Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 10 : min of the mean of all formants Frequency 2nd Derivative +% startOfROBUSTMeasurements + (nRF*nMetrics) + 11 : max of the mean of all formants Frequency 2nd Derivative +% + +noOfArguments = length(varargin); +columnIndices = []; + +getBURGFormants = 0; +getAllFormants=0; +getRobustFormants=0; + +for i=1 : noOfArguments + if( strcmp( varargin{i}, 'formant_Burg' )) + getBURGFormants = 1; + elseif( strcmp( varargin{i}, 'formant_all' )) + getAllFormants=1; + elseif( strcmp( varargin{i}, 'formant_robust' )) + getRobustFormants=1; + end +end + +titleName = ''; +for i=1 : noOfArguments + titleName = [ titleName varargin{i} '_']; + fprintf( masterFileOutputID, '%s_', varargin{i} ); +end + +fprintf( masterFileOutputID, '\t' ); + +% -------------------- get the data from the results file --------------- +lineCount = 0; +fileCount = 0; +data = []; +groups = []; + +while( ~(feof(inputFileID)) ) + + outputValues = []; + + thestr = fgetl(inputFileID); + if( lineCount > 10 ) % skip the file header + fileCount = fileCount + 1; + + % determine whether we have a positive or negative sample + sampleEmotion( fileCount ) = 'U'; + if( ~(isempty(strfind(thestr,'pos')))) + % sample is positive + sampleEmotion( fileCount ) = 'P'; + groups( fileCount ) = 1; + elseif( ~(isempty(strfind(thestr,'neg')))) + % sample is negative + sampleEmotion( fileCount ) = 'N'; + groups( fileCount ) = 0; + else + disp('EEEK!'); + pause; + end + +% % determine whether we have a male, female or trans sample +% gender( fileCount ) = '?'; +% if( ~(isempty(strfind(thestr,'fem')))) +% % gender is female +% gender( fileCount ) = 'F'; +% elseif( ~(isempty(strfind(thestr,'male')))) +% % gender is male +% gender( fileCount ) = 'M'; +% elseif( ~(isempty(strfind(thestr,'trans')))) +% % gender is trans +% gender( fileCount ) = 'T'; +% else +% disp('EEEK!'); +% pause; +% end + + %how many values are in the string? + spaces = strfind( thestr, ' ' ); + numberstr = thestr( spaces(1) : end ); % chop off the file name + frmtpos = strfind( numberstr, 'maxNoOfFormants'); % find the position of the label for number of formants + +% str1 = numberstr( 1 : frmtpos(1)-1 ); % string contains jitter and shimmer values + str2 = numberstr( frmtpos(1) : frmtpos(2)-1 ); % string contains all BURG formant information + str3 = numberstr( frmtpos(2) : frmtpos(3)-1 ); % string contains all ALL formant information + str4 = numberstr( frmtpos(3) : end ); % string contains all ROBUST formant information + + +% vars = sscanf( str1, '%f', inf ); +% % extract the shimmer and jitter values +% outputValues = [ outputValues vars( columnIndices )']; + + if( getBURGFormants ) + spaces = strfind( str2, ' ' ); % remove the string 'maxNoOfFormants' + vars = sscanf( str2( spaces(1) : end ), '%f', inf ); + outputValues = stripOutFormantValues( vars, outputValues ); + end + + if( getAllFormants ) + spaces = strfind( str3, ' ' ); % remove the string 'maxNoOfFormants' + vars = sscanf( str3( spaces(1) : end ), '%f', inf ); + outputValues = stripOutFormantValues( vars, outputValues ); + end + + if( getRobustFormants ) + spaces = strfind( str4, ' ' ); % remove the string 'maxNoOfFormants' + vars = sscanf( str4( spaces(1) : end ), '%f', inf ); + outputValues = stripOutFormantValues( vars, outputValues ); + end + + [m n] = size( data ); + % sometimes the 'all' formants command gives us fewer formants than + % usual. If this is the case,then we will have to pad with zeros + % for now. + if( n > length( outputValues ) ) + lenDiff = n - length( outputValues ); + outputValues = [ outputValues zeros( 1, lenDiff ) ]; + elseif( n < length( outputValues ) ) + lenDiff = length( outputValues ) - n; + outputValues = [ outputValues zeros( 1, lenDiff ) ]; + end + + data( fileCount, : ) = outputValues; + + end + lineCount = lineCount + 1; + +end +fclose(inputFileID); + + + +% ------------ apply the SVM classifier ------------------------ + +resultMatrix = []; + +noOfIterations = 10; + +for n = 1:noOfIterations + % Randomly select training and test sets, perhaps we should try all and + % choose the best? + [train, test] = crossvalind('holdOut',groups); + cp = classperf(groups); + + % Use a linear support vector machine classifier + svmStruct = svmtrain(data(train,:),groups(train)); + classes = svmclassify(svmStruct,data(test,:)); + % See how well the classifier performed + classperf(cp,classes,test); + numbers = cp.CountingMatrix; + + resultMatrix (n,:,:) = cp.DiagnosticTable; +% +end + + + + +% for emotion detection give the confusion matrix as + % ----------------------------------------------------------------- + % positive correctly identified | positive incorrectly identified (1,2) + % negative incorrectly identified (2,1) | negative correctly identified + % ------------------------------------------------------------------ + +% takes the average of 10 iterations - do we want to take the best? + +meanResults(1,1) = mean( resultMatrix(:,1,1) ); +meanResults(1,2) = mean( resultMatrix(:,2,1) ); +meanResults(2,1) = mean( resultMatrix(:,1,2) ); +meanResults(2,2) = mean( resultMatrix(:,2,2) ); + +meanResults(3,:)=0; +meanResults(:,3)=0; + +meanResults(3,3) = (meanResults(1,1) + meanResults(2,2));% / sum(sum(meanResults)); + +% convert to percentages +% how many of each sample do we have? +groupNumbers = unique( groups( test )); +groupNames = unique( sampleEmotion( test )); +sampleEmotionTest = sampleEmotion( test ); +% which group is which emotion? +thisGroupNumber = groupNumbers(1); +thisGroup = find( groups( test ) == thisGroupNumber ); +thisGroupName = unique( sampleEmotionTest( thisGroup )); + +thatGroupNumber = groupNumbers(2); +thatGroup = find( groups( test ) == thatGroupNumber ); +thatGroupName = unique( sampleEmotionTest( thatGroup )); + +if(length( thisGroupName ) ~= 1 ) + disp('ARGH!'); + pause; +end + +thisGroupNumberOfSamples = length( thisGroup ); +thatGroupNumberOfSamples = length( thatGroup ); + +if( thisGroupName == 'P' ) + %swap all the variables ready for checking + temp = thisGroupNumberOfSamples; + thisGroupNumberOfSamples = thatGroupNumberOfSamples; + thatGroupNumberOfSamples = temp; + + temp = thisGroupName; + thisGroupName = thatGroupName + thatGroupName = temp; + disp('CHECK ME!'); +end + +if( thisGroupName == 'N' ) + % group 0 is negative + if( sum( meanResults(1,:) ) == thisGroupNumberOfSamples ) + %if the elements in the first row add up to the number of negative + %samples, then swap the rows because we want the top row to be the + %results for the positive samples + temp(:,1) = meanResults(1:2,2); + temp(:,2) = meanResults(1:2,1); + temp2(1,:) = temp(2,:); + temp2(2,:) = temp(1,:); + + meanResults(1:2,1) = temp2(:,1); + meanResults(1:2,2) = temp2(:,2); + + % check the number of positive samples + if(( sum( meanResults(1,:) ) == thatGroupNumberOfSamples ) ... + && ( thatGroupName == 'P' ) ) + % row 1 is positive + disp('matrix correct'); + else + disp('ARGH!'); + pause; + end + + elseif( sum( meanResults(2,:) ) == thisGroupNumberOfSamples ) + + % the elements in the second row add up to the number of negative + % samples, so the matrix is the correct way around + + % check the number of positive samples + if(( sum( meanResults(1,:) ) == thatGroupNumberOfSamples ) ... + && ( thatGroupName == 'P' ) ) + % row 0 is positive + disp('matrix correct'); + else + disp('ARGH!'); + pause; + end + end +end + +% calculate the percentages +numberOfSamples = sum(sum( meanResults(1:2,1:2))); +percentageResults = meanResults; +percentageResults(1,1) = meanResults(1,1) / numberOfSamples; +percentageResults(1,2) = meanResults(1,2) / numberOfSamples; +percentageResults(2,1) = meanResults(2,1) / numberOfSamples; +percentageResults(2,2) = meanResults(2,2) / numberOfSamples; +percentageResults(3,3) = meanResults(3,3) / numberOfSamples; + +percentageResults = percentageResults * 100 + +confusionMatrix = percentageResults; + fprintf( masterFileOutputID, '\n %f \t %f \n %f \t %f \n %f \t %f \t %f \n', confusionMatrix(1,1), confusionMatrix(1,2), confusionMatrix(2,1), confusionMatrix(2,2), 0, 0, confusionMatrix(3,3)); + + % print latex results to the screen +str1 = sprintf(' & %2.2f & %2.2f & \\\\', percentageResults(1,1), percentageResults(1,2) ); +disp(str1); +str1 = sprintf(' & %2.2f & %2.2f & \\\\', percentageResults(2,1), percentageResults(2,2) ); +disp(str1); +str1 = sprintf(' & & & %2.2f \\\\',percentageResults(3,3) ); +disp(str1); + +fprintf( masterFileOutputID, '\n' ); +fclose( masterFileOutputID ); + +end + +%------------------------------------------------------------------ + +function [ outputValues ] = stripOutFormantValues( vars, outputValues ) + + noOfFormantValues = length( vars ) - 1; % gives the number of formant arguments only + noOfFormants = vars(1); + % there are 12 measurements for the mean of all formants (so the number + % of formants is not important) for each formant measurement. + if( noOfFormants ~= (noOfFormantValues-12)/24 ) + disp('EEK!'); + pause; + else + outputValues = [ outputValues vars( 2:end )' ]; + end + +end + +%-------------------------------------------------------------------