| << Chapter < Page | Chapter >> Page > |
Matlab code
% Blind Source Separation
% Four sounds are mixed together: (6 sec in total)% - Bach's Brandenburger concerto No.6, Allegro
% - CNN News Material% - Breakeven
% - Finnish Christmas Song: "A Sparrow on Christmas Morning"% Here we have four different mixing sounds samples. By performing ICA on
% them, we can successfully recover each individual sound.if isempty(which('fastica'))
display('add path of FastICA toolbox');addpath(strcat(pwd,'\FastICA_25'));
end% read sound files in
[x1,Fs]= audioread('Bach_mixed.wav'); % source #1 sound track
[x2,Fs]= audioread('CNN_mixed.wav',[1 size(x1,1)]); % source #2 sound track[x3,Fs] = audioread('Finnish_mixed.wav',[1 size(x1,1)]); % source #3 track
[x4,Fs]= audioread('Spanish_mixed.wav',[1 size(x1,1)]); % source #4 trackt = linspace(0,size(x1,1)/Fs,size(x1,1)); % time axis
% ICA analysis using FastICAx = [x1,x2,x3,x4]';r = fastica(x,'g','gauss');% The output levels of this algorithm are arbitrary, normalize to 1
r = r/max(max(abs(r)));% save output audio file
audiowrite('PC1_ica.wav',r(1,:),Fs);audiowrite('PC2_ica.wav',r(2,:),Fs);
audiowrite('PC3_ica.wav',r(3,:),Fs);audiowrite('PC4_ica.wav',r(4,:),Fs);
% comparison with Nonnegative Matrix Factorization[W,H] = nmf(x,8);H = H/max(max(abs(H)));
audiowrite('PC1_nmf.wav',H(1,:),Fs);audiowrite('PC2_nmf.wav',H(2,:),Fs);
audiowrite('PC3_nmf.wav',H(3,:),Fs);audiowrite('PC4_nmf.wav',H(4,:),Fs);
% comparison with PCA[U,D,V] = svds(x,8);V = V/max(max(abs(V))); %normalize
audiowrite('PC1_pca.wav',V(:,1),Fs);audiowrite('PC2_pca.wav',V(:,2),Fs);
audiowrite('PC3_pca.wav',V(:,3),Fs);audiowrite('PC4_pca.wav',V(:,4),Fs);
% Handwritten Digit Recognition% recognize number 8
data = csvread('train8.txt');% plotting some images
colormap('gray')for i=1:12
subplot(3,4,i)mat = flipud(rot90(reshape(data(i,:),16,16)));
imagesc(mat)axis off
endfigure(3)
%NMFaddpath('~/matlab/nmf_toolbox_ver1.4')
[W,H]= nmf(zscore(data),12);
colormap('gray')for i=1:8
subplot(3,8,i)mat = flipud(rot90(reshape(H(i,:),16,16)));
imagesc(mat)axis off
end%ICA
addpath('~/matlab/FastICA_2.5/FastICA_25')[S,A,Wh] = fastica(zscore(data),'lastEig',12,'g','tanh','maxNumIterations',1e6);colormap('gray')
for i=1:8subplot(3,8,i+8)
mat = flipud(rot90(reshape(S(i,:),16,16)));imagesc(mat)
axis offend
%PCA[U,D,V] = svds(zscore(data),8);colormap('gray')
for i=1:8subplot(3,8,i+16)
mat = flipud(rot90(reshape(V(:,i),16,16)));imagesc(mat)
axis offend
Matlab Code for Blind Source Separation and Hand-written Digit Recognition
Notification Switch
Would you like to follow the 'Comparison of three different matrix factorization techniques for unsupervised machine learning' conversation and receive update notifications?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|