diff examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m @ 155:b14209313ba4 ivand_dev

Integration of Majorization Minimisation Dictionary Learning

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+++ b/examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m	Mon Aug 22 11:46:35 2011 +0100
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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 = generateAMT_Learning_Problem('',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) SMALL_midiGenerate(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_cgp';
+
+%   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) SMALL_midiGenerate(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, versn] = 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
+
+
+