daniele@169: clc, clear, close all
daniele@169: 
daniele@169: %% Parameteres
daniele@169: % Dictionary learning parameters
daniele@169: toolbox   = 'TwoStepDL';							%dictionary learning toolbox
daniele@169: dicUpdate = {'ksvd'};								%dictionary learning updates
daniele@169: dicDecorr = {'none','mailhe','tropp','barchiesi'};	%dictionary decorrelation methods
daniele@169: minCoherence = linspace(0.1,1,1);					%coherence levels
daniele@169: minCoherence = 0.4;
daniele@169: %dicDecorr = {'barchiesi'};
daniele@169: 
daniele@169: iterNum   = 20;				%number of iterations
daniele@169: epsilon   = 1e-6;			%tolerance level
daniele@169: dictSize  = 512;			%number of atoms in the dictionary
daniele@169: percActiveAtoms = 5;		%percentage of active atoms
daniele@169: 
daniele@169: % Test signal parameters
daniele@169: signal    = audio('music03_16kHz.wav'); %audio signal
daniele@169: blockSize = 256;						%size of audio frames
daniele@169: overlap   = 0.5;						%overlap between consecutive frames
daniele@169: 
daniele@169: 
daniele@169: % Dependent parameters
daniele@169: nActiveAtoms = fix(blockSize/100*percActiveAtoms); %number of active atoms
daniele@169: 
daniele@169: % Initial dictionaries
daniele@169: dctDict = dictionary('dct',blockSize,dictSize);
daniele@169: dctDict = dctDict.phi;
daniele@169: gaborParam = struct('N',blockSize,'redundancyFactor',2,'wd',@rectwin);
daniele@169: gaborDict = Gabor_Dictionary(gaborParam);
daniele@169: initDicts = {[],dctDict,gaborDict};
daniele@169: initDicts = {[]};
daniele@169: 
daniele@169: %% Generate audio approximation problem
daniele@169: signal			 = buffer(signal,blockSize,blockSize*overlap,@rectwin);
daniele@169: SMALL.Problem.b  = signal.S;
daniele@169: SMALL.Problem.b1 = SMALL.Problem.b; % copy signals from training set b to test set b1 (needed for later functions)
daniele@169: 
daniele@169: % omp2 sparse representation solver
daniele@169: ompParam = struct('X',SMALL.Problem.b,'epsilon',epsilon,'maxatoms',nActiveAtoms); %parameters
daniele@169: solver	 = SMALL_init_solver('ompbox','omp2',ompParam,false); %solver structure
daniele@169: 
daniele@169: 
daniele@169: %% Test
daniele@169: nDicUpdates = length(dicUpdate);		%number of dictionary updates
daniele@169: nDecorrAlgs = length(dicDecorr);		%number of decorrelation algorithms
daniele@169: nCorrLevels = length(minCoherence);		%number of coherence levels
daniele@169: nInitDicts  = length(initDicts);		%number of initial dictionaries
daniele@169: 
daniele@169: SMALL.DL(nInitDicts,nCorrLevels,nDecorrAlgs,nDicUpdates) = SMALL_init_DL(toolbox); %create dictionary learning structures
daniele@169: for iInitDicts=1:nInitDicts
daniele@169: 	for iCorrLevels=1:nCorrLevels
daniele@169: 		for iDecorrAlgs=1:nDecorrAlgs
daniele@169: 			for iDicUpdates=1:nDicUpdates
daniele@169: 				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).toolbox = toolbox;
daniele@169: 				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).name = dicUpdate{iDicUpdates};
daniele@169: 				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).profile = true;
daniele@169: 				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).param = ...
daniele@169: 					struct( 'data',SMALL.Problem.b,...
daniele@169: 					'Tdata',nActiveAtoms,...
daniele@169: 					'dictsize',dictSize,...
daniele@169: 					'iternum',iterNum,...
daniele@169: 					'memusage','high',...
daniele@169: 					'solver',solver,...
daniele@169: 					'decFcn',dicDecorr{iDecorrAlgs},...
daniele@169: 					'coherence',minCoherence(iCorrLevels),...
daniele@169: 					'initdict',initDicts(iInitDicts));
daniele@169: 				
daniele@169: 				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
daniele@169: 					SMALL_learn(SMALL.Problem,SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates));
daniele@169: 			end
daniele@169: 		end
daniele@169: 	end
daniele@169: end
daniele@169: 
daniele@169: %% Evaluate coherence and snr of representation for the various methods
daniele@169: sr = zeros(size(SMALL.DL));				%signal to noise ratio
daniele@169: mu = zeros(size(SMALL.DL));				%coherence
daniele@169: dic(size(SMALL.DL)) = dictionary;		%initialise dictionary objects
daniele@169: for iInitDict=1:nInitDicts
daniele@169: 	for iCorrLevels=1:nCorrLevels
daniele@169: 		for iDecorrAlgs=1:nDecorrAlgs
daniele@169: 			for iDicUpdates=1:nDicUpdates
daniele@169: 				%Sparse representation
daniele@169: 				SMALL.Problem.A = SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D;
daniele@169: 				tempSolver = SMALL_solve(SMALL.Problem,solver);
daniele@169: 				%calculate snr
daniele@169: 				sr(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
daniele@169: 					snr(SMALL.Problem.b,SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D*tempSolver.solution);
daniele@169: 				%calculate mu
daniele@169: 				dic(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
daniele@169: 					dictionary(SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D);
daniele@169: 				mu(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
daniele@169: 					dic(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).coherence;
daniele@169: 			end
daniele@169: 		end
daniele@169: 	end
daniele@169: end
daniele@169: 
daniele@169: %% Plot results
daniele@169: minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1)));	%lowe bound on coherence
daniele@169: initDictsNames = {'Data','DCT','Gabor'};						
daniele@169: dicDecorrNames = {'K-SVD','INK-SVD','Grassmannian','New'};
daniele@169: lineStyles     = {'ks-','kd-','ko-','k*-'};
daniele@169: for iInitDict=1:nInitDicts
daniele@169: 	figure, hold on, grid on
daniele@169: 	title([initDictsNames{iInitDict} ' Initialisation']);
daniele@169: 	plot([1 1],[0 25],'k-');
daniele@169: 	for iDecorrAlgs=1:nDecorrAlgs
daniele@169: 		plot(mu(iInitDicts,:,iDecorrAlgs,1),sr(iInitDicts,:,iDecorrAlgs,1),...
daniele@169: 			lineStyles{iDecorrAlgs});
daniele@169: 	end
daniele@169: 	plot([minMu minMu],[0 25],'k--')
daniele@169: 	
daniele@169: 	set(gca,'YLim',[0 25],'XLim',[0 1.4]);
daniele@169: 	legend([{'\mu_{max}'},dicDecorrNames,{'\mu_{min}'}]);
daniele@169: 	xlabel('\mu');
daniele@169: 	ylabel('SNR (dB)');
daniele@169: end