Mercurial > hg > smallbox
diff examples/SMALL_test_coherence.m @ 156:a4d0977d4595 danieleb
First branch commit, danieleb
author | danieleb |
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date | Tue, 30 Aug 2011 11:12:31 +0100 |
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children | 8324c7ea6602 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/examples/SMALL_test_coherence.m Tue Aug 30 11:12:31 2011 +0100 @@ -0,0 +1,210 @@ +clear + +%% Parameteres + +% Dictionary learning parameters +toolbox = 'TwoStepDL'; %dictionary learning toolbox +dicUpdate = {'ksvd','mailhe'}; %dictionary updates +iterNum = 20; %number of iterations + +% Test signal parameters +signal = audio('music03_16kHz.wav'); %audio signal +blockSize = 256; %size of audio frames +dictSize = 512; %number of atoms in the dictionary +overlap = 0.5; %overlap between consecutive frames +sigma = 1e6; %snr of noise (set to be negligible so that the problem becomes approximation rather than denoising) +percActiveAtoms = 5; %percentage of active atoms + +% Dependent parameters +nActiveAtoms = fix(blockSize/100*percActiveAtoms); %number of active atoms +epsilon = 1/sigma; %error constraint for sparse representation step (corresponds to noise applied to signals) +minCoherence = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); %target coherence (based on coherence lower bound) + +% Initial dictionaries +dctDict = dictionary('dct',blockSize,dictSize); +dctDict = dctDict.phi; +gaborParam = struct('N',blockSize,'redundancyFactor',2,'wd',@rectwin); +gaborDict = Gabor_Dictionary(gaborParam); + +%% Generate audio denoising problem with low noise (audio representation) +SMALL.Problem = generateAudioDenoiseProblem(signal.s,[],blockSize,... + dictSize,overlap,sigma); % generate representation problem +SMALL.Problem.b1 = SMALL.Problem.b; % copy signals from training set b to test set b1 (needed for later functions) + +% omp2 sparse representation solver +ompParam = struct('X',SMALL.Problem.b,'epsilon',epsilon,'maxatoms',nActiveAtoms); %parameters +solver = SMALL_init_solver('ompbox','omp2',ompParam,false); %solver structure + + +%% Test ksvd dictionary update +name = dicUpdate{1}; %use ksvd update +SMALL.DL(1:9) = SMALL_init_DL(toolbox,name); %create dictionary learning structures + +% learn with random initialisation and no decorrelation +SMALL.DL(1).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none'); %parameters for the dictionary learning +SMALL.DL(1) = SMALL_learn(SMALL.Problem,SMALL.DL(1)); %learn dictionary + +% learn with random initialisation and mailhe decorrelation +SMALL.DL(2).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning +SMALL.DL(2) = SMALL_learn(SMALL.Problem,SMALL.DL(2)); %learn dictionary + +% learn with random initialisation and tropp decorrelation +SMALL.DL(3).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning +SMALL.DL(3) = SMALL_learn(SMALL.Problem,SMALL.DL(3)); %learn dictionary + +% Learn with dct initialisation and no decorrelation +SMALL.DL(4).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(4) = SMALL_learn(SMALL.Problem,SMALL.DL(4)); %learn dictionary + +% learn with dct initialisation and mailhe decorrelation +SMALL.DL(5).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(5) = SMALL_learn(SMALL.Problem,SMALL.DL(5)); %learn dictionary + +% learn with dct initialisation and tropp decorrelation +SMALL.DL(6).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(6) = SMALL_learn(SMALL.Problem,SMALL.DL(6)); %learn dictionary + +% Learn with gabor initialisation and no decorrelation +SMALL.DL(7).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(7) = SMALL_learn(SMALL.Problem,SMALL.DL(7)); %learn dictionary + +% learn with gabor initialisation and mailhe decorrelation +SMALL.DL(8).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(8) = SMALL_learn(SMALL.Problem,SMALL.DL(8)); %learn dictionary + +% learn with gabor initialisation and tropp decorrelation +SMALL.DL(9).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(9) = SMALL_learn(SMALL.Problem,SMALL.DL(9)); %learn dictionary + +%% Test mailhe dictionary update +name = dicUpdate{2}; %use mailhe update +SMALL.DL(10:18) = SMALL_init_DL(toolbox,name); %create dictionary learning structure + +% learn with random initialisation and no decorrelation +SMALL.DL(10).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none'); %parameters for the dictionary learning +SMALL.DL(10) = SMALL_learn(SMALL.Problem,SMALL.DL(10)); %learn dictionary + +% learn with random initialisation and mailhe decorrelation +SMALL.DL(11).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning +SMALL.DL(11) = SMALL_learn(SMALL.Problem,SMALL.DL(11)); %learn dictionary + +% learn with random initialisation and tropp decorrelation +SMALL.DL(12).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning +SMALL.DL(12) = SMALL_learn(SMALL.Problem,SMALL.DL(12)); %learn dictionary + +% Learn with dct initialisation and no decorrelation +SMALL.DL(13).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(13) = SMALL_learn(SMALL.Problem,SMALL.DL(13)); %learn dictionary + +% learn with dct initialisation and mailhe decorrelation +SMALL.DL(14).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(14) = SMALL_learn(SMALL.Problem,SMALL.DL(14)); %learn dictionary + +% learn with dct initialisation and tropp decorrelation +SMALL.DL(15).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(15) = SMALL_learn(SMALL.Problem,SMALL.DL(15)); %learn dictionary + +% Learn with gabor initialisation and no decorrelation +SMALL.DL(16).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(16) = SMALL_learn(SMALL.Problem,SMALL.DL(16)); %learn dictionary + +% learn with gabor initialisation and mailhe decorrelation +SMALL.DL(17).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(17) = SMALL_learn(SMALL.Problem,SMALL.DL(17)); %learn dictionary + +% learn with gabor initialisation and tropp decorrelation +SMALL.DL(18).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(18) = SMALL_learn(SMALL.Problem,SMALL.DL(18)); %learn dictionary + +%% Evaluate coherence and snr of representation for the various methods +sigNoiseRatio = zeros(18,1); +mu = zeros(18,1); +for i=1:18 + SMALL.Problem.A = SMALL.DL(i).D; + tempSolver = SMALL_solve(SMALL.Problem,solver); + sigNoiseRatio(i) = snr(SMALL.Problem.b,SMALL.DL(i).D*tempSolver.solution); + dic(i) = dictionary(SMALL.DL(i).D); + mu(i) = dic(i).coherence; +end + +%% Plot results +minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); +maxSNR = max(sigNoiseRatio); + +figure, subplot(2,2,1) +snrMat = buffer(sigNoiseRatio(1:9),3); +bar(snrMat'); +title('SNR - KSVD Update') +xlabel('Initial dictionary') +ylabel('SNR (dB)') +set(gca,'XTickLabel',{'data','dct','gabor'},'YLim',[0 maxSNR+1]); +legend('none','Mailhe','Tropp') +grid on + +subplot(2,2,2), grid on +snrMat = buffer(sigNoiseRatio(10:18),3); +bar(snrMat'); +title('SNR - Mailhe Update') +xlabel('Initial dictionary') +ylabel('SNR (dB)') +set(gca,'XTickLabel',{'data','dct','gabor'},'YLim',[0 maxSNR+1]); +legend('none','Mailhe','Tropp') +grid on + +subplot(2,2,3), hold on, grid on +title('Coherence - KSVD Update') +muMat = buffer(mu(1:9),3); +line([0.5 3.5],[1 1],'Color','r'); +bar(muMat'); +line([0.5 3.5],[minMu minMu],'Color','k'); +set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05]) +legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}') +ylabel('\mu') +xlabel('Initial dictionary') + +subplot(2,2,4), hold on, grid on +title('Coherence - Mailhe Update') +muMat = buffer(mu(10:18),3); +line([0.5 3.5],[1 1],'Color','r'); +bar(muMat'); +line([0.5 3.5],[minMu minMu],'Color','k'); +set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05]) +legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}') +ylabel('\mu') +xlabel('Initial dictionary')