Dawn@4: function [] = get_MFCCS( dirName, statsFileID )
Dawn@4: 
Dawn@4: 
Dawn@4: % this function collates the results of all values calculated for the
Dawn@4: % MFCCs
Dawn@4: 
Dawn@4: DEBUG=0;
Dawn@4: 
Dawn@4:     % identify the speaker in the stats file
Dawn@4:     fprintf( statsFileID, '%s ', dirName );
Dawn@4:     
Dawn@4:     metricName = '_MFCC';
Dawn@4:     metricFileName = [ dirName metricName '.txt'];
Dawn@4:     numberOfValuesPerFrame = 13; % 13 MFCC's
Dawn@4:     
Dawn@4:     FileID = fopen( metricFileName );
Dawn@4:     
Dawn@4:     %---------------- GET THE VOICED FRAME VALUES -------------------
Dawn@4: 
Dawn@4:     % only wish to consider values from voiced frames. The MFCCs were
Dawn@4:     % calculate for only non-silent frames anyway, but now we need to
Dawn@4:     % remove the unvoiced frame values
Dawn@4:     
Dawn@4:     % going to disregard all unvoiced frames as these produce outliers
Dawn@4:     [vuv] = detect_VoicedUnvoiced( [ dirName '.wav' ], 0 );
Dawn@4:     
Dawn@4:     % get the silent frames
Dawn@4:     segmentFrames = detect_Silence( [ dirName '.wav' ], 0 );
Dawn@4:     [ silentFrames ] = getSilentDataArray( segmentFrames, length(vuv) );
Dawn@4:     
Dawn@4:     % remove silent frames from vuv
Dawn@4:     non_silentFrames = find(silentFrames == 1);
Dawn@4:     vuv = vuv( non_silentFrames );
Dawn@4: %     voicedFrames = find( vuv == 1 );
Dawn@4: 
Dawn@4:     if( FileID <= 0 ) %does the file exist?
Dawn@4:         % if not
Dawn@4:         message = ['WARNING: MISSING ' metricName ' FILE'];
Dawn@4:         disp( message );
Dawn@4:         fprintf( statsFileID, ' \s missing', metricName );
Dawn@4: 
Dawn@4:     else
Dawn@4:         mfcc = readManyValuesFromFile( statsFileID, metricFileName, metricName, numberOfValuesPerFrame );
Dawn@4:         % smooth to get rid of outliers
Dawn@4: %         for( i=1:numberOfValuesPerFrame )
Dawn@4: % %             smoothedMfcc(:,i) = smooth( mfcc( voicedFrames,i ) );
Dawn@4: %             smoothedMfcc(:,i) = smooth( mfcc( :,i ) );
Dawn@4: %         end
Dawn@4:         
Dawn@4:         for( i=1:numberOfValuesPerFrame )
Dawn@4:             % find the basic metric set for each frequency band
Dawn@4: %             [ metrics(i,:) ] = basicMetricSet( smoothedMfcc(:,i), statsFileID );
Dawn@4:             [ metrics(i,:) ] = basicMetricSet( mfcc(:,i), statsFileID );
Dawn@4:             mean(i) = metrics(i,1);
Dawn@4:         end
Dawn@4:         
Dawn@4:         % find the gradient of each frequency band and its basic metric set
Dawn@4:         [m,n]=size(metrics);
Dawn@4:         for( i=1:numberOfValuesPerFrame )
Dawn@4: %             gradSmoothedMfcc(:,i) = gradient( smoothedMfcc(:,i) );
Dawn@4:            gradMfcc(:,i) = gradient( mfcc(:,i) );
Dawn@4: %             [ metrics(i, n+1:2*n ) ] = basicMetricSet( gradSmoothedMfcc(:,i), statsFileID );
Dawn@4:             [ metrics(i, n+1:2*n ) ] = basicMetricSet( gradMfcc(:,i), statsFileID );
Dawn@4:         end
Dawn@4:         
Dawn@4:         % calculate the basic metric set for the mean of all frequency
Dawn@4:         % bands
Dawn@4:         basicMetricSet( mean, statsFileID );
Dawn@4:     end
Dawn@4:     
Dawn@4:     
Dawn@4:     fprintf( statsFileID, '\n');
Dawn@4:     fclose(FileID);