Mercurial > hg > smallbox
view examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m @ 187:3cc204120431 luisf_dev
Merge from branch "bug_386"
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
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date | Thu, 09 Feb 2012 17:26:45 +0000 |
parents | 9c418bea7f6a |
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%% Dictionary Learning for Automatic Music Transcription - KSVD vs SPAMS % % % This file contains an example of how SMALLbox can be used to test diferent % dictionary learning techniques in Automatic Music Transcription problem. % It calls generateAMT_Learning_Problem that will let you to choose midi, % wave or mat file to be transcribe. If file is midi it will be first % converted to wave and original midi file will be used for comparison with % results of dictionary learning and reconstruction. % The function will generarte the Problem structure that is used to learn % Problem.p notes spectrograms from training set Problem.b using % dictionary learning technique defined in DL structure. % Two dictionary learning techniques were compared: % % - KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient % Implementation of the K-SVD Algorithm using Batch Orthogonal % Matching Pursuit", Technical Report - CS, Technion, April 2008. % % - MMDL - M. Yaghoobi, T. Blumensath and M. Davies, "Dictionary Learning % for Sparse Approximations with the Majorization Method", IEEE % Trans. on Signal Processing, Vol. 57, No. 6, pp 2178-2191, % 2009. % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2011 Ivan Damnjanovic. % % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License as % published by the Free Software Foundation; either version 2 of the % License, or (at your option) any later version. See the file % COPYING included with this distribution for more information. %% clear; % Defining Automatic Transcription of Piano tune as Dictionary Learning % Problem SMALL.Problem = generateAMTProblem('',2048,0.75); %% % Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in % SMALL.Problem.p) using sparsity constrain only % Initialising Dictionary structure % Setting Dictionary structure fields (toolbox, name, param, D and time) % to zero values SMALL.DL(1)=SMALL_init_DL(); % Defining fields needed for dictionary learning SMALL.DL(1).toolbox = 'KSVD'; SMALL.DL(1).name = 'ksvd'; % Defining the parameters for KSVD % In this example we are learning 88 atoms in 100 iterations, so that % every frame in the training set can be represented with maximum Tdata % dictionary elements. Type help ksvd in MATLAB prompt for more options. SMALL.DL(1).param=struct(... 'Tdata', 5,... 'dictsize', SMALL.Problem.p,... 'iternum', 50); % Learn the dictionary SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1)); % Set SMALL.Problem.A dictionary and reconstruction function % (backward compatiblity with SPARCO: solver structure communicate % only with Problem structure, ie no direct communication between DL and % solver structures) SMALL.Problem.A = SMALL.DL(1).D; SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem); %% % Initialising solver structure % Setting solver structure fields (toolbox, name, param, solution, % reconstructed and time) to zero values % As an example, SPAMS (Julien Mairal 2009) implementation of LARS % algorithm is used for representation of training set in the learned % dictionary. SMALL.solver(1)=SMALL_init_solver; % Defining the parameters needed for sparse representation SMALL.solver(1).toolbox='SMALL'; SMALL.solver(1).name='SMALL_pcgp'; % Here we use mexLasso mode=2, with lambda=2, lambda2=0 and positivity % constrain (type 'help mexLasso' for more information about modes): % % min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2 SMALL.solver(1).param='20, 1e-2'; % struct(... % 'lambda', 2,... % 'pos', 1,... % 'mode', 2); % Call SMALL_soolve to represent the signal in the given dictionary. % As a final command SMALL_solve will call above defined reconstruction % function to reconstruct the training set (Problem.b) in the learned % dictionary (Problem.A) SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1)); %% % Analysis of the result of automatic music transcription. If groundtruth % exists, we can compare transcribed notes and original and get usual % True Positives, False Positives and False Negatives measures. if ~isempty(SMALL.Problem.notesOriginal) AMT_res(1) = AMT_analysis(SMALL.Problem, SMALL.solver(1)); end %% % % Here we solve the same problem using non-negative sparse coding with % % SPAMS online dictionary learning (Julien Mairal 2009) % % % Initialising solver structure % Setting solver structure fields (toolbox, name, param, solution, % reconstructed and time) to zero values % As an example, SPAMS (Julien Mairal 2009) implementation of LARS % algorithm is used for representation of training set in the learned % dictionary. SMALL.solver(2)=SMALL_init_solver; % Defining the parameters needed for sparse representation SMALL.solver(2).toolbox='SPAMS'; SMALL.solver(2).name='mexLasso'; % Here we use mexLasso mode=2, with lambda=3, lambda2=0 and positivity % constrain (type 'help mexLasso' for more information about modes): % % min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2 SMALL.solver(2).param=struct('lambda', 3, 'pos', 1, 'mode', 2); % You can also test ALPS, IST from MMbox or any other solver, but results % are not as good as SPAMS % % % Initialising solver structure % % Setting solver structure fields (toolbox, name, param, solution, % % reconstructed and time) to zero values % % SMALL.solver(2)=SMALL_init_solver; % % % Defining the parameters needed for image denoising % % SMALL.solver(2).toolbox='ALPS'; % SMALL.solver(2).name='AlebraicPursuit'; % % SMALL.solver(2).param=struct(... % 'sparsity', 10,... % 'memory', 1,... % 'mode', 6,... % 'iternum', 100,... % 'tau',-1,... % 'tolerance', 1e-14',... % 'verbose',1); % % Initialising Dictionary structure % % Setting Dictionary structure fields (toolbox, name, param, D and time) % % to zero values % % Initialising solver structure % % Setting solver structure fields (toolbox, name, param, solution, % % reconstructed and time) to zero values % % SMALL.solver(2)=SMALL_init_solver; % % % Defining the parameters needed for image denoising % % SMALL.solver(2).toolbox='MMbox'; % SMALL.solver(2).name='mm1'; % SMALL.solver(2).param=struct(... % 'lambda',50,... % 'iternum',1000,... % 'map',0); SMALL.DL(2)=SMALL_init_DL('MMbox', 'MM_cn', '', 1); % Defining the parameters for Majorization Minimization dictionary update % % In this example we are learning 88 atoms in 200 iterations, so that SMALL.DL(2).param=struct(... 'solver', SMALL.solver(2),... 'initdict', SMALL.Problem.A,... 'dictsize', SMALL.Problem.p,... 'iternum', 200,... 'iterDictUpdate', 1000,... 'epsDictUpdate', 1e-7,... 'cvset',0,... 'show_dict', 0); % Learn the dictionary SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2)); % Set SMALL.Problem.A dictionary and reconstruction function % (backward compatiblity with SPARCO: solver structure communicate % only with Problem structure, ie no direct communication between DL and % solver structures) SMALL.Problem.A = SMALL.DL(2).D; SMALL.Problem.reconstruct=@(x) AMT_reconstruct(x, SMALL.Problem); % Call SMALL_soolve to represent the signal in the given dictionary. % As a final command SMALL_solve will call above defined reconstruction % function to reconstruct the training set (Problem.b) in the learned % dictionary (Problem.A) SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2)); % Analysis of the result of automatic music transcription. If groundtruth % exists, we can compare transcribed notes and original and get usual % True Positives, False Positives and False Negatives measures. if ~isempty(SMALL.Problem.notesOriginal) AMT_res(2) = AMT_analysis(SMALL.Problem, SMALL.solver(2)); end % Plot results and save midi files if ~isempty(SMALL.Problem.notesOriginal) figAMT = SMALL_AMT_plot(SMALL, AMT_res); else figAMT = figure('Name', 'Automatic Music Transcription KSVD vs SPAMS'); subplot(2,1,1); plot(SMALL.solver(1).reconstructed.notes(:,5), SMALL.solver(1).reconstructed.notes(:,3), 'kd '); title (sprintf('%s dictionary in %.2f s', SMALL.DL(1).name, SMALL.DL(1).time)); xlabel('Time'); ylabel('Note Number'); subplot(2,1,2); plot(SMALL.solver(2).reconstructed.notes(:,5), SMALL.solver(2).reconstructed.notes(:,3), 'b* '); title (sprintf('%s dictionary in %.2f s', SMALL.DL(2).name, SMALL.DL(2).time)); xlabel('Time'); ylabel('Note Number'); end FS=filesep; [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m')); cd([pathstr1,FS,'results']); [filename,pathname] = uiputfile({' *.mid;' },'Save KSVD result midi'); if filename~=0 writemidi(SMALL.solver(1).reconstructed.midi, [pathname,FS,filename]);end [filename,pathname] = uiputfile({' *.mid;' },'Save SPAMS result midi'); if filename~=0 writemidi(SMALL.solver(2).reconstructed.midi, [pathname,FS,filename]);end [filename,pathname] = uiputfile({' *.fig;' },'Save KSVD vs SPAMS AMT figure'); if filename~=0 saveas(figAMT, [pathname,FS,filename]);end