Continue to Site

Welcome to EDAboard.com

Welcome to our site! EDAboard.com is an international Electronics Discussion Forum focused on EDA software, circuits, schematics, books, theory, papers, asic, pld, 8051, DSP, Network, RF, Analog Design, PCB, Service Manuals... and a whole lot more! To participate you need to register. Registration is free. Click here to register now.

Help needed in matlab code

Status
Not open for further replies.

mzoh

Newbie level 4
Joined
Nov 2, 2017
Messages
6
Helped
0
Reputation
0
Reaction score
0
Trophy points
1
Activity points
63
Hello,

I am working on audio classification using svm and i found this code from link:

https://github.com/mosamdabhi/Voice...n-Speech-Recognition-System-Machine-Learning-

but its gving errors, data set is not shared in this code, i am using other data set, in svmtrain i am getting errors about

thisLab = str2num(Characterstring(1:end-4));
trainedSVM = svmtrain(net_TrainingSet,net_Labels);
error: svmtrain Y and TRAINING must have the same number of rows

i have made some changes in this code like wavread changed my audioread and some other function, please help me out to run this code.



Code:
% Author: Mosam

clc;
clear all;
close all;

listing = dir('C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train1\*.wav');

for g=1:length(listing);
    names{g} = listing(g).name;
end

labels=[];
TrainingSet=[];
Accuracy=[];

for xx=1:length(listing);
    [x,Fs] = audioread([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train1\' names{xx}]);
    
    
    
    %N = length(x); % signal length
    %n = 0:N-1;
    %ts = n*(1/Fs); % time for signal
    
    %wintype = 'rectwin';
    Nlen = 320;
    Nshft = 160;
    
    steng=calsteng(x,Nlen,Nshft);
    
    Characterstring = names{xx};
    Characterstring = Characterstring(1:end-4);
    thisLab = str2num(Characterstring(1:end-4));
    labels = [labels thisLab];
    GroupVariable = labels';
    
    
    
    %labels_row = cellstr(R)
    %y=1/x;
    
    %disp([names{xx} num2str(length(steng))])
    InitDiff = diff(steng(1:length(steng)));
    
    InitDiffthreshold = (1/100)*max(steng);
    max(InitDiff);
    %M = max(find(abs(T) > (t)));
    stengmin = min(find(abs(InitDiff) > (InitDiffthreshold)));
    
    FlippedInitDiff = fliplr(InitDiff);
    max(FlippedInitDiff);
    Thresholdcut = find(abs(FlippedInitDiff) > (InitDiffthreshold), 1 );
    stengmax = length(FlippedInitDiff)-Thresholdcut;
    
    
    factor = length(x)/length(steng);
    
    %i = stengmax;
    xlimupper = factor*stengmax;
    
    %j = stengmin;
    xlimlower = factor*stengmin;
    
    
    ax1 = subplot(3,1,1);
    plot(x); hold on;
    %# vertical line
    hx = plot(xlimlower, 'LineStyle',':', 'Color',[.7 .7 .7]);
   % changedependvar(hx,'x');
    hx = plot(xlimupper, 'LineStyle',':', 'Color',[.7 .7 .7]);
  %  changedependvar(hx,'x');
    hold off;
    
    title(names{xx})
    
    
    
    subplot(3,1,2);
    plot(steng,'r');
    
    
    ax2 = subplot(3,1,3);
    %axis([3500,9625,-1,1]);
    plot(x);
    xlim(ax2,[xlimlower xlimupper]);
    
    Tw = 25;                % analysis frame duration (ms)
    Ts = 10;                % analysis frame shift (ms)
    alpha = 0.97;           % preemphasis coefficient
    M = 20;                 % number of filterbank channels
    C = 12;                 % number of cepstral coefficients
    L = 22;                 % cepstral sine lifter parameter
    LF = 300;               % lower frequency limit (Hz)
    HF = 3700;              % upper frequency limit (Hz)
    wav_file = ([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train1\' names{xx}]);   % input audio filename
    
    [ speech, fs ] = audioread( wav_file );
    
    
    % Feature extraction (feature vectors as columns)
%     [ MFCCs, FBEs, frames ] = ...
%         mfcc( speech, fs, Tw, Ts, alpha, @hamming, [LF HF], M, C+1, L );
    [ MFCCs, FBEs, frames ] = ...
                    mfcc( speech, fs, Tw, Ts, alpha, @hamming, [LF HF], M, C+1, L );
    
    % Generate data needed for plotting
    [ Nw, NF ] = size( frames );                % frame length and number of frames
    time_frames = [0:NF-1]*Ts*0.001+0.5*Nw/fs;  % time vector (s) for frames
    time = [ 0:length(speech)-1 ]/fs;           % time vector (s) for signal samples
    logFBEs = 20*log10( FBEs );                 % compute log FBEs for plotting
    logFBEs_floor = max(logFBEs(:))-50;         % get logFBE floor 50 dB below max
    logFBEs( logFBEs<logFBEs_floor ) = logFBEs_floor; % limit logFBE dynamic range
    
    
    % Generate plots
    %figure('Position', [30 30 800 600], 'PaperPositionMode', 'auto', ...
     %   'color', 'w', 'PaperOrientation', 'landscape', 'Visible', 'on' );
    
    ax2 = subplot(3,1,1);
    plot(x);
    xlim(ax2,[xlimlower xlimupper])
    xlabel( 'Time (s)' );
    ylabel( 'Amplitude' );
    title( 'Speech waveform');
    
    subplot(3,1,2);
    imagesc( time_frames, [1:M], logFBEs );
    axis( 'xy' );
    xlim( [ min(time_frames) max(time_frames) ] );
    xlabel( 'Time (s)' );
    ylabel( 'Channel index' );
    title( 'Log (mel) filterbank energies');
    
    subplot(3,1,3);
    imagesc( time_frames, [1:C], MFCCs(2:end,:) ); % HTK's TARGETKIND: MFCC
    %imagesc( time_frames, [1:C+1], MFCCs );       % HTK's TARGETKIND: MFCC_0
    %R = imagesc
    
    axis( 'xy' );
    xlim( [ min(time_frames) max(time_frames) ] )
    xlabel( 'Time (s)' );
    ylabel( 'Cepstrum index' );
    title( 'Mel frequency cepstrum' );
    Energy = MFCCs';
    R1 = Energy;
    R1 = R1(1:15:length(Energy),:);
    
    
    %Energy = MFCCs(4,:);
    %R2 = Energy';
    %R1 = R2;
    
    %Feature1 = var(R1);
    %Feature2 = mean(R1);
    %CovarianceFeat = cov(R1);
    %Feature3 = CovarianceFeat(:)';
    %Feature4 = std(R1);
    %CorrcoefFeat = corrcoef(R1);
    %Feature5 = CorrcoefFeat(:)';
    %Feature6 = mode(R1);
    %Feature7 = median(R1);
    %Feature8 = min(R1);
    %Feature9 = max(R1);
    %NetFeatures = [Feature1 Feature2 Feature3 Feature4 Feature6 Feature7 Feature8 Feature9];
    
    
    
    TrainingSet = [R1; TrainingSet];
    
    
end

labels_rows = GroupVariable(:);
labels_appending_matrix = [];
for i1=1:size(TrainingSet,1)
    labels_appending_matrix = [labels_rows; labels_appending_matrix]; 
end

listing3 = dir('C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train2\*.wav');

for g3=1:length(listing3); 
    names{g3} = listing3(g3).name;
end

TrainingSet_Train2=[];
labels3=[];


for xx3=1:length(listing3);
    [x3,Fs] = audioread([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train2\' names{xx3}]);
    
    
    
    %N = length(x); % signal length
    %n = 0:N-1;
    %ts = n*(1/Fs); % time for signal
    
    %wintype = 'rectwin';
    Nlen = 320;
    Nshft = 160;
    
    steng3=calsteng(x3,Nlen,Nshft);
    
    Characterstring3 = names{xx3};
    Characterstring3 = Characterstring3(1:end-4);
    thisLab3 = str2num([Characterstring3(:)]);
    labels3 = [labels3 thisLab3];
    GroupVariable3 = labels3';
    
    
    InitDiff3 = diff(steng3(1:length(steng3)));
    
    InitDiffthreshold3 = (1/100)*max(steng3);
    max(InitDiff3);
%     M = max(find(abs(T) > (t)));
    stengmin3 = find(abs(InitDiff3) > (InitDiffthreshold3), 1 );
    
    FlippedInitDiff3 = fliplr(InitDiff3);
    max(FlippedInitDiff3);
    Thresholdcut3 = find(abs(FlippedInitDiff3) > (InitDiffthreshold3), 1 );
    stengmax3 = length(FlippedInitDiff3)-Thresholdcut3;
    
    
    factor3 = length(x3)/length(steng3);
    
    %i = stengmax;
    xlimupper3 = factor3*stengmax3;
    
    %j = stengmin;
    xlimlower3 = factor3*stengmin3;
    
    
    ax123 = subplot(3,1,1);
    plot(x3); hold on;
    %# vertical line
    hx3 = plot(xlimlower3, 'LineStyle',':', 'Color',[.7 .7 .7]);
%     changedependvar(hx3,'x');
    hx3 = plot(xlimupper3, 'LineStyle',':', 'Color',[.7 .7 .7]);
   % changedependvar(hx3,'x');
    hold off;
    
    title(names{xx3})
    
    
    
    subplot(3,1,2);
    plot(steng3,'r');
    
    
    ax223 = subplot(3,1,3);
    %axis([3500,9625,-1,1]);
    plot(x3);
    xlim(ax223,[xlimlower3 xlimupper3])
    
    Tw = 25;                % analysis frame duration (ms)
    Ts = 10;                % analysis frame shift (ms)
    alpha = 0.97;           % preemphasis coefficient
    M = 20;                 % number of filterbank channels
    C = 12;                 % number of cepstral coefficients
    L = 22;                 % cepstral sine lifter parameter
    LF = 300;               % lower frequency limit (Hz)
    HF = 3700;              % upper frequency limit (Hz)
    wav_file = ([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Train2\' names{xx3}]);   % input audio filename
    
    [ speech3, fs3 ] = audioread( wav_file );
    
    
    % Feature extraction (feature vectors as columns)
    [ MFCCs3, FBEs3, frames3 ] = ...
        mfcc( speech3, fs3, Tw, Ts, alpha, @hamming, [LF HF], M, C+1, L );
    
    
    % Generate data needed for plotting
    [ Nw3, NF3 ] = size( frames3 );                % frame length and number of frames
    time_frames3 = [0:NF3-1]*Ts*0.001+0.5*Nw3/fs3;  % time vector (s) for frames
    time3 = [ 0:length(speech3)-1 ]/fs3;           % time vector (s) for signal samples
    logFBEs3 = 20*log10( FBEs3 );                 % compute log FBEs for plotting
    logFBEs_floor3 = max(logFBEs3(:))-50;         % get logFBE floor 50 dB below max
    logFBEs3( logFBEs3<logFBEs_floor3 ) = logFBEs_floor3; % limit logFBE dynamic range
    
    
    % Generate plots
    %figure('Position', [30 30 800 600], 'PaperPositionMode', 'auto', ...
     %   'color', 'w', 'PaperOrientation', 'landscape', 'Visible', 'on' );
    
    ax223 = subplot(3,1,1);
    plot(x3);
    xlim(ax223,[xlimlower3 xlimupper3]);
    xlabel( 'Time (s)' );
    ylabel( 'Amplitude' );
    title( 'Speech waveform');
    
    subplot(3,1,2);
    imagesc( time_frames3, [1:M], logFBEs3 );
    axis( 'xy' );
    xlim( [ min(time_frames3) max(time_frames3) ] );
    xlabel( 'Time (s)' );
    ylabel( 'Channel index' );
    title( 'Log (mel) filterbank energies');
    
    subplot(3,1,3);
    imagesc( time_frames3, [1:C], MFCCs3(2:end,:) ); % HTK's TARGETKIND: MFCC
    %imagesc( time_frames, [1:C+1], MFCCs );       % HTK's TARGETKIND: MFCC_0
    %R = imagesc
    
    axis( 'xy' );
    xlim( [ min(time_frames3) max(time_frames3) ] );
    xlabel( 'Time (s)' );
    ylabel( 'Cepstrum index' );
    title( 'Mel frequency cepstrum' );
    Energy3 = MFCCs3';
    R3 = Energy3;
    R3 = R3(1:15:length(Energy3),:);
    
    %R223 = Energy3';
    
    
    %R123 = R223;
    
    %Feature1_3 = var(R123);
    %Feature2_3 = mean(R123);
    %CovarianceFeat = cov(R123);
    %Feature3_3 = CovarianceFeat(:)';
    %Feature4_3 = std(R123);
    %CorrcoefFeat = corrcoef(R12);
    %Feature5_2 = CorrcoefFeat(:)';
    %Feature6_3 = mode(R123);
    %Feature7_3 = median(R123);
    %Feature8_3 = min(R123);
    %Feature9_3 = max(R123);
    %NetFeatures1_3 = [Feature1_3 Feature2_3 Feature3_3 Feature4_3 Feature6_3 Feature7_3 Feature8_3 Feature9_3];
    
    
    
    TrainingSet_Train2 = [R3; TrainingSet_Train2];
 
    
end


labels_rows3 = GroupVariable3(:);
labels_appending_matrix3 = [];
for i3=1:size(TrainingSet_Train2,1)
    labels_appending_matrix3 = [labels_rows3; labels_appending_matrix3]; 
end

net_TrainingSet = [TrainingSet; TrainingSet_Train2];
net_Labels = [labels_appending_matrix; labels_appending_matrix3];
trainedSVM = svmtrain(net_TrainingSet,net_Labels);


testSet = dir('C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Test*.wav');

for g2=1:length(testSet); 
    names{g2} = testSet(g2).name;
end

TrainingSet_Test=[];
output=[];
labels2=[];
for xx2=1:length(testSet);
    [x2,Fs] = audioread([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Test\' names{xx2}]);
    
    
    
    %N = length(x); % signal length
    %n = 0:N-1;
    %ts = n*(1/Fs); % time for signal
    
    %wintype = 'rectwin';
    Nlen = 320;
    Nshft = 160;
    
    steng2=calsteng(x2,Nlen,Nshft);
    
    Characterstring2 = names{xx2};
    Characterstring2 = Characterstring2(1:end-4);
    thisLab2 = str2num([Characterstring2(4)]);
    labels2 = [labels2 thisLab2];
    GroupVariable2 = labels2';
    
    
    InitDiff2 = diff(steng2(1:length(steng2)));
    
    InitDiffthreshold2 = (1/100)*max(steng2);
    max(InitDiff2);
    %M = max(find(abs(T) > (t)));
    stengmin2 = min(find(abs(InitDiff2) > (InitDiffthreshold2)));
    
    FlippedInitDiff2 = fliplr(InitDiff2);
    max(FlippedInitDiff2);
    Thresholdcut2 = min(find(abs(FlippedInitDiff2) > (InitDiffthreshold2)));
    stengmax2 = length(FlippedInitDiff2)-Thresholdcut2;
    
    
    factor2 = length(x2)/length(steng2);
    
    %i = stengmax;
    xlimupper2 = factor2*stengmax2;
    
    %j = stengmin;
    xlimlower2 = factor2*stengmin2;
    
    
    ax12 = subplot(3,1,1);
    plot(x2); hold on;
    %# vertical line
    hx2 = plot(xlimlower2, 'LineStyle',':', 'Color',[.7 .7 .7]);
    %changedependvar(hx2,'x');
    hx2 = plot(xlimupper2, 'LineStyle',':', 'Color',[.7 .7 .7]);
    %changedependvar(hx2,'x');
    hold off;
    
    title(names{xx2})
    
    
    
    subplot(3,1,2);
    plot(steng2,'r');
    
    
    ax22 = subplot(3,1,3);
    %axis([3500,9625,-1,1]);
    plot(x2);
    xlim(ax22,[xlimlower2 xlimupper2])
    
    Tw = 25;                % analysis frame duration (ms)
    Ts = 10;                % analysis frame shift (ms)
    alpha = 0.97;           % preemphasis coefficient
    M = 20;                 % number of filterbank channels
    C = 12;                 % number of cepstral coefficients
    L = 22;                 % cepstral sine lifter parameter
    LF = 300;               % lower frequency limit (Hz)
    HF = 3700;              % upper frequency limit (Hz)
    wav_file = ([ 'C:\Users\hp\Downloads\Compressed\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Voice-Based-Digit-Recognition-Speech-Recognition-System-Machine-Learning--master\Test\' names{xx2}]);   % input audio filename
    
    [ speech2, fs2 ] = audioread( wav_file );
    
    
    % Feature extraction (feature vectors as columns)
    [ MFCCs2, FBEs2, frames2 ] = ...
        mfcc( speech2, fs2, Tw, Ts, alpha, @hamming, [LF HF], M, C+1, L );
    
    
    % Generate data needed for plotting
    [ Nw2, NF2 ] = size( frames2 );                % frame length and number of frames
    time_frames2 = [0:NF2-1]*Ts*0.001+0.5*Nw2/fs2;  % time vector (s) for frames
    time2 = [ 0:length(speech2)-1 ]/fs2;           % time vector (s) for signal samples
    logFBEs2 = 20*log10( FBEs2 );                 % compute log FBEs for plotting
    logFBEs_floor2 = max(logFBEs2(:))-50;         % get logFBE floor 50 dB below max
    logFBEs2( logFBEs2<logFBEs_floor2 ) = logFBEs_floor2; % limit logFBE dynamic range
    
    
    % Generate plots
    %figure('Position', [30 30 800 600], 'PaperPositionMode', 'auto', ...
     %   'color', 'w', 'PaperOrientation', 'landscape', 'Visible', 'on' );
    
    ax22 = subplot(3,1,1);
    plot(x2);
    xlim(ax22,[xlimlower2 xlimupper2]);
    xlabel( 'Time (s)' );
    ylabel( 'Amplitude' );
    title( 'Speech waveform');
    
    subplot(3,1,2);
    imagesc( time_frames2, [1:M], logFBEs2 );
    axis( 'xy' );
    xlim( [ min(time_frames2) max(time_frames2) ] );
    xlabel( 'Time (s)' );
    ylabel( 'Channel index' );
    title( 'Log (mel) filterbank energies');
    
    subplot(3,1,3);
    imagesc( time_frames2, [1:C], MFCCs2(2:end,:) ); % HTK's TARGETKIND: MFCC
    %imagesc( time_frames, [1:C+1], MFCCs );       % HTK's TARGETKIND: MFCC_0
    %R = imagesc
    
    axis( 'xy' );
    xlim( [ min(time_frames2) max(time_frames2) ] );
    xlabel( 'Time (s)' );
    ylabel( 'Cepstrum index' );
    title( 'Mel frequency cepstrum' );
     
    Energy2 = MFCCs2'
    %Energy2 = MFCCs2';
    %R2 = Energy2;
    %R2 = R2(1:15:length(Energy2),:);
    
    
    
    
    TrainingSet_Test = [Energy2; TrainingSet_Test];
 
   

    
end

%net_TrainingSet = [TrainingSet; TrainingSet_Train2];


labels_rows2 = GroupVariable2(1,:);
labels_appending_matrix2 = [];
for i2=1:size(TrainingSet_Test,1)
    labels_appending_matrix2 = [labels_rows2; labels_appending_matrix2]; 
end



%trainedSVM = svmtrain(net_TrainingSet,net_Labels);
for i=1:size(TrainingSet_Test,1)
    tmp = svmclassify(trainedSVM,TrainingSet_Test(i,:));
    output = [output tmp];
end
 %TRY=[];
 output = mode(output)
 %acctf = ((output-labels2)./labels2);
 %accsum = sum(((acctf')/i)*100);
 %accuracy = 100 - accsum;
 TRY = [output' labels_appending_matrix2(1,:)];
 EVAL = Evaluate(labels_appending_matrix2(1,:),output'); 
 %stats = confusionmatStats(labels2,output);

%Accuracy = [output-labels]
%[svmstruct,level] = Train_DSVM(TrainingSet,GroupVariable)
 
Last edited by a moderator:

Status
Not open for further replies.

Part and Inventory Search

Welcome to EDABoard.com

Sponsor

Back
Top