annotate examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m @ 25:cbf3521c25eb

(none)
author idamnjanovic
date Tue, 27 Apr 2010 13:33:13 +0000
parents f72603404233
children 8e660fd14774
rev   line source
idamnjanovic@6 1 %% DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
idamnjanovic@25 2 %
idamnjanovic@25 3 % Centre for Digital Music, Queen Mary, University of London.
idamnjanovic@25 4 % This file copyright 2010 Ivan Damnjanovic.
idamnjanovic@25 5 %
idamnjanovic@25 6 % This program is free software; you can redistribute it and/or
idamnjanovic@25 7 % modify it under the terms of the GNU General Public License as
idamnjanovic@25 8 % published by the Free Software Foundation; either version 2 of the
idamnjanovic@25 9 % License, or (at your option) any later version. See the file
idamnjanovic@25 10 % COPYING included with this distribution for more information.
idamnjanovic@25 11 %
idamnjanovic@6 12 % This file contains an example of how SMALLbox can be used to test diferent
idamnjanovic@6 13 % dictionary learning techniques in Automatic Music Transcription problem.
idamnjanovic@6 14 % It calls generateAMT_Learning_Problem that will let you to choose midi,
idamnjanovic@6 15 % wave or mat file to be transcribe. If file is midi it will be first
idamnjanovic@6 16 % converted to wave and original midi file will be used for comparison with
idamnjanovic@6 17 % results of dictionary learning and reconstruction.
idamnjanovic@6 18 % The function will generarte the Problem structure that is used to learn
idamnjanovic@6 19 % Problem.p notes spectrograms from training set Problem.b using
idamnjanovic@6 20 % dictionary learning technique defined in DL structure.
idamnjanovic@6 21 %
idamnjanovic@6 22 %%
idamnjanovic@6 23
idamnjanovic@6 24 clear;
idamnjanovic@6 25
idamnjanovic@6 26
idamnjanovic@6 27 % Defining Automatic Transcription of Piano tune as Dictionary Learning
idamnjanovic@6 28 % Problem
idamnjanovic@6 29
idamnjanovic@6 30 SMALL.Problem = generateAMT_Learning_Problem();
idamnjanovic@6 31
idamnjanovic@6 32 TPmax=0;
idamnjanovic@6 33
idamnjanovic@6 34 for i=1:10
idamnjanovic@6 35
idamnjanovic@6 36 %%
idamnjanovic@6 37 % Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in
idamnjanovic@6 38 % SMALL.Problem.p) using sparsity constrain only
idamnjanovic@6 39
idamnjanovic@6 40 % Initialising Dictionary structure
idamnjanovic@6 41 % Setting Dictionary structure fields (toolbox, name, param, D and time)
idamnjanovic@6 42 % to zero values
idamnjanovic@6 43
idamnjanovic@6 44 SMALL.DL(i)=SMALL_init_DL(i);
idamnjanovic@6 45
idamnjanovic@6 46 % Defining fields needed for dictionary learning
idamnjanovic@6 47
idamnjanovic@6 48 SMALL.DL(i).toolbox = 'KSVD';
idamnjanovic@6 49 SMALL.DL(i).name = 'ksvd';
idamnjanovic@6 50
idamnjanovic@6 51 % Defining the parameters for KSVD
idamnjanovic@6 52 % In this example we are learning 88 atoms in 100 iterations.
idamnjanovic@6 53 % our aim here is to show how individual parameters can be tested in
idamnjanovic@6 54 % the AMT problem. We test ten different values for sparity (Tdata)
idamnjanovic@6 55 % in KSVD algorithm.
idamnjanovic@6 56 % Type help ksvd in MATLAB prompt for more options.
idamnjanovic@6 57 Tdata(i)=i;
idamnjanovic@6 58 SMALL.DL(i).param=struct('Tdata', Tdata(i), 'dictsize', SMALL.Problem.p, 'iternum', 100);
idamnjanovic@6 59
idamnjanovic@6 60 % Learn the dictionary
idamnjanovic@6 61
idamnjanovic@6 62 SMALL.DL(i) = SMALL_learn(SMALL.Problem, SMALL.DL(i));
idamnjanovic@6 63
idamnjanovic@6 64 % Set SMALL.Problem.A dictionary and reconstruction function
idamnjanovic@6 65 % (backward compatiblity with SPARCO: solver structure communicate
idamnjanovic@6 66 % only with Problem structure, ie no direct communication between DL and
idamnjanovic@6 67 % solver structures)
idamnjanovic@6 68
idamnjanovic@6 69 SMALL.Problem.A = SMALL.DL(i).D;
idamnjanovic@6 70 SMALL.Problem.reconstruct = @(x) SMALL_midiGenerate(x, SMALL.Problem);
idamnjanovic@6 71
idamnjanovic@6 72 %%
idamnjanovic@6 73 % Initialising solver structure
idamnjanovic@6 74 % Setting solver structure fields (toolbox, name, param, solution,
idamnjanovic@6 75 % reconstructed and time) to zero values
idamnjanovic@6 76 % As an example, SPAMS (Julien Mairal 2009) implementation of LARS
idamnjanovic@6 77 % algorithm is used for representation of training set in the learned
idamnjanovic@6 78 % dictionary.
idamnjanovic@6 79
idamnjanovic@6 80 SMALL.solver(1)=SMALL_init_solver;
idamnjanovic@6 81
idamnjanovic@6 82 % Defining the parameters needed for sparse representation
idamnjanovic@6 83
idamnjanovic@6 84 SMALL.solver(1).toolbox='SPAMS';
idamnjanovic@6 85 SMALL.solver(1).name='mexLasso';
idamnjanovic@6 86
idamnjanovic@6 87 %%
idamnjanovic@6 88 % Initialising solver structure
idamnjanovic@6 89 % Setting solver structure fields (toolbox, name, param, solution,
idamnjanovic@6 90 % reconstructed and time) to zero values
idamnjanovic@6 91 % As an example, SPAMS (Julien Mairal 2009) implementation of LARS
idamnjanovic@6 92 % algorithm is used for representation of training set in the learned
idamnjanovic@6 93 % dictionary.
idamnjanovic@6 94
idamnjanovic@6 95 SMALL.solver(1).param=struct(...
idamnjanovic@6 96 'lambda', 2,...
idamnjanovic@6 97 'pos', 1,...
idamnjanovic@6 98 'mode', 2);
idamnjanovic@6 99
idamnjanovic@6 100 % Call SMALL_soolve to represent the signal in the given dictionary.
idamnjanovic@6 101 % As a final command SMALL_solve will call above defined reconstruction
idamnjanovic@6 102 % function to reconstruct the training set (Problem.b) in the learned
idamnjanovic@6 103 % dictionary (Problem.A)
idamnjanovic@6 104
idamnjanovic@6 105
idamnjanovic@6 106 SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
idamnjanovic@6 107
idamnjanovic@6 108 %%
idamnjanovic@6 109 % Analysis of the result of automatic music transcription. If groundtruth
idamnjanovic@6 110 % exists, we can compare transcribed notes and original and get usual
idamnjanovic@6 111 % True Positives, False Positives and False Negatives measures.
idamnjanovic@6 112
idamnjanovic@6 113 AMT_res(i) = AMT_analysis(SMALL.Problem, SMALL.solver(1));
idamnjanovic@6 114 if AMT_res(i).TP>TPmax
idamnjanovic@6 115 TPmax=AMT_res(i).TP;
idamnjanovic@6 116 BLmidi=SMALL.solver(1).reconstructed.midi;
idamnjanovic@6 117 max=i;
idamnjanovic@6 118 end
idamnjanovic@6 119 end % end of for loop
idamnjanovic@6 120
idamnjanovic@6 121 %%
idamnjanovic@6 122 % Plot results and save midi files
idamnjanovic@6 123
idamnjanovic@6 124 figAMTbest=SMALL_AMT_plot(SMALL, AMT_res(max));
idamnjanovic@6 125
idamnjanovic@6 126 resFig=figure('Name', 'Automatic Music Transcription KSVD Sparsity TEST');
idamnjanovic@6 127
idamnjanovic@6 128 subplot (3,1,1); plot(Tdata(:), [AMT_res(:).TP], 'ro-');
idamnjanovic@6 129 title('True Positives vs Tdata');
idamnjanovic@6 130
idamnjanovic@6 131 subplot (3,1,2); plot(Tdata(:), [AMT_res(:).FN], 'ro-');
idamnjanovic@6 132 title('False Negatives vs Tdata');
idamnjanovic@6 133
idamnjanovic@6 134 subplot (3,1,3); plot(Tdata(:), [AMT_res(:).FP], 'ro-');
idamnjanovic@6 135 title('False Positives vs Tdata');
idamnjanovic@6 136
idamnjanovic@6 137 FS=filesep;
idamnjanovic@6 138 [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
idamnjanovic@6 139 cd([pathstr1,FS,'results']);
idamnjanovic@6 140 [filename,pathname] = uiputfile({' *.mid;' },'Save midi');
idamnjanovic@6 141 if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
idamnjanovic@6 142 [filename,pathname] = uiputfile({' *.fig;' },'Save figure TP/FN/FP vs Tdata');
idamnjanovic@6 143 if filename~=0 saveas(resFig, [pathname,FS,filename]);end
idamnjanovic@6 144
idamnjanovic@6 145 [filename,pathname] = uiputfile({' *.fig;' },'Save BEST AMT figure');
idamnjanovic@6 146 if filename~=0 saveas(figAMTbest, [pathname,FS,filename]);end
idamnjanovic@6 147