comparison Sirtassa/Main.m @ 2:13ec2fa02a26 tip

(none)

+%% Default Values
+%Audio and score information
+if(~exist('midiCell','var'))
+    midiCell = cell(2,1);
+    midiCell{1} = 'Lussier_bassoon.mid';
+    midiCell{2} = 'Lussier_trumpet.mid';
+    mixName = 'Lussier_mix.wav';
+    %mixName = '311CLNOF.WAV';
+    %midiCell{1} = '311CLNOF.mid';
+end
+if(~exist('blockSize','var'))   blockSize = 2048;   end
+if(~exist('overlap','var'))     overlap = 2;        end
+%Maximum frequency of the filter
+if(~exist('bandPassFilterMaxFreq','var'))    bandPassFilterMaxFreq = 10000; end
+%Number of band pass filters in the filter
+if(~exist('nbBandPassFilters','var'))    nbBandPassFilters = 20;end
+%Maximum updates per frame
+if(~exist('maxUpdates','var'))    maxUpdates = 2;end
+%Number of partials in the model
+if(~exist('nbPartials','var'))    nbPartials = 8;end
+if(~exist('plotRefinedScore','var'))    plotRefinedScore = 0;end
+if(~exist('plotPartial','var'))    plotPartial = 0;end
+if(~exist('plotFilter','var'))    plotFilter = 0;end
+%Nummber of instrument in the mix.
+nbInstrument = length(midiCell);
+fftSize = blockSize;
+
+%Weights of the harmonic series
+partialsWeights = ones(nbPartials,nbInstrument);
+
+colors = 'rwm';
+%% Initialisation
+hopSize =blockSize/overlap;                   %Hopsize
+HFFTS = fftSize/2+1;                    %Size of the usefull part of the fft (half+1)
+[audio, fs] = wavread(mixName);
+
+% zeropadding the audio length
+audio(end:end+hopSize-mod(length(audio),hopSize)) = 0;
+audioLength = length(audio);
+
+scoreLength = audioLength/hopSize - overlap+1; %Number of frames
+wdw = hamming(blockSize);
+
+tic
+%% Create filters
+filterBasis = createBPFilters(bandPassFilterMaxFreq,nbBandPassFilters,fftSize,fftSize/fs);
+filterCoefficients = ones(nbBandPassFilters,nbInstrument);
+%% Creating the score
+originalScore = createScore(scoreLength,midiCell,fs/hopSize,fftSize/fs);
+originalScore(scoreLength+1:end,:) = [];
+gains = double(originalScore'>0);
+%% Creating spectrogram
+magnitude = zeros(fftSize, scoreLength);
+phase = zeros(fftSize, scoreLength);
+audioVector = zeros(fftSize,1);
+for curFrame = 1:scoreLength
+    audioVector(1:blockSize) = audio(hopSize*(curFrame-1)+1:hopSize*(curFrame-1)+blockSize,1).*wdw;
+    spectrogramVector = fft(audioVector);
+    magnitude(:,curFrame) = abs(spectrogramVector);
+    phase(:,curFrame) = angle(spectrogramVector);
+end
+magnitude(HFFTS+1:end,:) = [];
+phase(HFFTS+1:end,:) = [];
+maxSizeWindows = 400;
+hammingWindows = cell(1,maxSizeWindows);
+for len = 1:maxSizeWindows
+    hammingWindows{len} = hamming(len);
+end
+
+%% Refine Score and create Excitation Spectrum
+refinedScore = originalScore;   %score to be refiened
+basicSpec = zeros(HFFTS, scoreLength);        %Basic theoretical spectrogram
+excitationSpectrums = cell(nbInstrument,scoreLength);   %cell for each frame
+largerES = cell(nbInstrument,scoreLength);   %cell for each frame
+
+for ins = 1:nbInstrument                %for each isntrument
+    for curFrame = 1: scoreLength        %for each frame
+        excitationSpectrums{ins,curFrame} = sparse(zeros(HFFTS,nbPartials));
+        largerES{ins,curFrame} = sparse(zeros(HFFTS,nbPartials));
+        if(originalScore(curFrame,ins))        %if there is a note
+            %Refine score and creates Excitation Spectrum
+            [refinedScore(curFrame,ins), width] = refineScore(magnitude(:,curFrame),originalScore(curFrame,ins));
+            excitationSpectrums{ins,curFrame} = sparse(createExcitationSpectrums(refinedScore(curFrame,ins),HFFTS,nbPartials,partialsWeights(:,ins),width,hammingWindows));
+            largerES{ins,curFrame} = sparse(createExcitationSpectrums(refinedScore(curFrame,ins),HFFTS,nbPartials,partialsWeights(:,ins),2*width,hammingWindows));
+        end
+        basicSpec(:,curFrame) = basicSpec(:,curFrame) + (excitationSpectrums{ins,curFrame}*partialsWeights(:,ins))*gains(ins,curFrame); %Spectrogram without update
+    end
+end
+%% Plot spectrogram and add original and refined score
+if (plotRefinedScore)
+    figure(1),imagesc(log(10*magnitude(1:fftSize/16,:)+1)), axis xy
+    plotableScore = originalScore+1;
+    plotableScore(plotableScore==1) = 0;
+    hold on ,plot(plotableScore,'.k'),
+    
+    for harm = 1 : nbPartials
+        for ins = nbInstrument:-1:1
+            plot(refinedScore(:,ins)*harm+1,['.' colors(ins)]), colorbar
+        end
+    end
+end
+%% Online Processing
+%Values that can be changed by the model.
+maskForChangeableValues = basicSpec > 10^-4;
+maskedSpectrogram = magnitude.*maskForChangeableValues;
+bufferSize = 100;
+nextProcess = 1;
+for curFrame = 1:scoreLength
+    if sum(refinedScore(curFrame,:),2)
+        UpdateGainCoefficientsOL2
+        if (curFrame==nextProcess)
+            first = max(curFrame-bufferSize+1,1);
+            currentMag = magnitude(:,first:curFrame);
+            tmpMaskForChangeableValues = basicSpec(:,first:curFrame) > 10^-4;
+            tmpMaskedSpectrogram = currentMag.*tmpMaskForChangeableValues;
+            len = curFrame-first+1;
+            for  nbUpdates = 1:maxUpdates           %update loop
+                UpdatePartialCoefficientsOL
+                UpdateFilterCoefficientsOL
+                if (plotFilter)
+                    %%%Filter
+                    figure(4),plot(outlook(1:blockSize/8,:)), title('Filter')
+                end
+                if (plotPartial)
+                    %%%Decay
+                    figure(5),stem(partialsWeights), title('Partial Weights')
+                end
+            end
+            nextProcess = min(ceil(curFrame*1.2),scoreLength)
+            %bufferSize = round(max(bufferSize,curFrame*0.5));
+            if (plotFilter)
+                %%%Filter
+                figure(4),plot(outlook(1:blockSize/8,:)), title('Filter')
+            end
+            if (plotPartial)
+                %%%Decay
+                figure(5),stem(partialsWeights), title('Partial Weights')
+                
+            end
+        end
+    end
+end
+timeV = toc;
+fprintf('Time: %6.3g s, %6.2g times real time \t \n ', timeV, timeV*fs/audioLength);
+
+
+% Remodel Spectrogram
+ECA = cell(nbInstrument,1);
+xHat = zeros(HFFTS,scoreLength);
+for ins = 1:nbInstrument
+    ECA{ins} = sparse(zeros(HFFTS,scoreLength));
+    for frame = 1:scoreLength
+        ECA{ins}(:,frame) = filterBasis * filterCoefficients(:,ins).*(largerES{ins,frame}*partialsWeights(:,ins));
+    end
+    xHat = xHat + ECA{ins}.*repmat(gains(ins,:),HFFTS,1);
+end
+%% Final plot
+%%%Spectrogram (comparison with basic and final)
+% % bMax = max(max(log(10*basicSpec+1)));
+% % xMax = max(max(log(10*xHat+1)));
+% % mMax = max(max(log(10*magnitude+1)));
+% % tMax = max([bMax mMax xMax]);
+% % figure(3), subplot(3,1,1), imagesc(log(10*basicSpec(1:hopSize/2,:)+1)), axis xy, linkaxes, caxis([0 tMax]), colorbar, title('Basic Model')
+% % subplot(3,1,2), imagesc(log(10*xHat(1:hopSize/2,:)+1)), axis xy, linkaxes, caxis([0 tMax]), colorbar, title('Optimised Spectrogram')
+% % subplot(3,1,3), imagesc(log(10*magnitude(1:hopSize/2,:)+1)), axis xy, linkaxes, caxis([0 tMax]), colorbar, title('Actual Spectrogram')
+% %
+% %
+% % %%%Filter
+% % figure(4),plot(outlook(1:blockSize/8,:)), title('Filter')
+% %
+% % %%%Decay
+% % figure(5),stem(partialsWeights), title('Partial Weights')
+
+%% Writing
+%extraInfo = [sprintf('_%d_%d_%d_%d_%d_%d',blockSize,overlap,maxUpdates,nbPartials,nbBandPassFilters,bandPassFilterMaxFreq) specExp];
+finalScale = zeros(audioLength,1);
+freq = zeros(fftSize,1);
+finalAudio = cell(nbInstrument+1,1);
+finalAudioMasked = cell(nbInstrument,1);
+
+wdw2 = hamming(fftSize);
+nbMaskingTechniques = 4;
+maskingCell = cell(1,nbMaskingTechniques);
+maskingCell{1} = 'raw';
+maskingCell{2} = 'per';
+maskingCell{3} = 'sha';
+maskingCell{4} = '10p';
+finalMask = cell(nbInstrument,nbMaskingTechniques); %4 is the number of different masking techniques
+for ins = 1:nbInstrument
+    finalMask{ins,1} = sparse(zeros(HFFTS,scoreLength));
+    finalMask{ins,4} = sparse(zeros(HFFTS,scoreLength));
+    for curFrame = 1:scoreLength
+        finalMask{ins,1}(:,curFrame) = filterBasis * filterCoefficients(:,ins).*(excitationSpectrums{ins,curFrame}*partialsWeights(:,ins));
+        finalMask{ins,4}(:,curFrame) = filterBasis * filterCoefficients(:,ins).*(largerES{ins,curFrame}*partialsWeights(:,ins));
+    end
+    
+    finalMask{ins,1} = finalMask{ins,1}.*repmat(gains(ins,:),HFFTS,1);
+    finalMask{ins,4} = finalMask{ins,4}.*repmat(gains(ins,:),HFFTS,1);
+    
+    % masking strategy 1
+    finalMask{ins,2} = magnitude .* (finalMask{ins,4} ./ (xHat+eps));
+    
+    % masking strategy 2
+    finalMask{ins,3} = magnitude .* ( (finalMask{ins,4}+eps) ./ (xHat+eps));
+    
+    % masking strategy 3
+    finalMask{ins,4} = magnitude .* ( (finalMask{ins,4}+0.1 * eps) ./ (xHat+eps));
+    
+    finalAudio{ins} = zeros(audioLength,1);
+    finalAudioMasked{ins} = zeros(audioLength,1);
+end
+
+% sonifying the model
+finalAudio{nbInstrument+1} = zeros(audioLength,1);
+%Overlap and add
+for curFrame = 1:scoreLength
+    freq(1:HFFTS) = xHat(:,curFrame).*exp(1i*phase(:,curFrame));
+    freq(HFFTS+1:fftSize) = freq(HFFTS-1:-1:2);
+    faudio = real(ifft(freq)).*wdw2;
+    finalAudio{nbInstrument+1}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) = finalAudio{nbInstrument+1}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) + faudio;
+    for ins = 1:nbInstrument
+        freq(1:HFFTS) = finalMask{ins,1}(:,curFrame).*exp(1i*phase(:,curFrame));
+        freq(HFFTS+1:fftSize) = freq(HFFTS-1:-1:2);
+        faudio = real(ifft(freq)).*wdw2;
+        finalAudio{ins}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) = finalAudio{ins}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) + faudio;
+    end
+    finalScale((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) = finalScale((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) + wdw2;
+end
+
+for ins = 1:nbInstrument
+    finalAudio{ins} = finalAudio{ins}./finalScale;
+    %    wavwrite(finalAudio{ins},fs,16,[midiCell{ins}(1:4) '_' num2str(ins) '_' maskingCell{1} extraInfo '.wav']);
+end
+finalAudio{nbInstrument+1} = finalAudio{nbInstrument+1}./finalScale;
+%wavwrite(finalAudio{nbInstrument+1},fs,16,[mixName(1:4) '_0_' maskingCell{1} extraInfo '.wav']);
+
+% sonifying the masked version
+%Overlap and add
+for tech = 2:nbMaskingTechniques
+    finalScale = zeros(audioLength,1);
+    for curFrame = 1:scoreLength
+        for ins = 1:nbInstrument
+            freq(1:HFFTS) = finalMask{ins,tech}(:,curFrame).*exp(i*phase(:,curFrame));
+            freq(HFFTS+1:fftSize) = freq(HFFTS-1:-1:2);
+            faudio = real(ifft(freq)).*wdw2;
+            finalAudioMasked{ins}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) = finalAudioMasked{ins}((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) + faudio;
+        end
+        finalScale((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) = finalScale((curFrame-1)*hopSize+1:(curFrame-1)*hopSize+fftSize,1) + wdw2;
+    end
+    for ins = 1:nbInstrument
+        finalAudioMasked{ins} = finalAudioMasked{ins}./finalScale;
+        %    wavwrite(finalAudioMasked{ins},fs,16,[midiCell{ins}(1:4) '_' num2str(ins) '_' maskingCell{tech} extraInfo '.wav']);
+    end
+end
+
+
+