Mercurial > hg > smallbox
view examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m @ 247:ecce33192fcc tip
Added tag ver_2.1 for changeset cef4500b936f
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
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date | Wed, 31 Oct 2012 12:24:44 +0000 |
parents | 9c418bea7f6a |
children |
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%% Dictionary Learning for Automatic Music Transcription - KSVD residual %% error test % % *WARNING!* You should have SPAMS in your search path in order for this % script to work.Due to licensing issues SPAMS can not be automatically % provided in SMALLbox (http://www.di.ens.fr/willow/SPAMS/downloads.html). % % 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. % % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2010 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(); TPmax=0; for i=1:5 %% % 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(i)=SMALL_init_DL(i); % Defining the parameters needed for dictionary learning SMALL.DL(i).toolbox = 'KSVD'; SMALL.DL(i).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 10 % dictionary elements. However, our aim here is to show how individual % parameters can be ested in the AMT problem. We test five different % values for residual error (Edata) in KSVD algorithm. % Type help ksvd in MATLAB prompt for more options. Edata(i)=8+i*2; SMALL.DL(i).param=struct(... 'Edata', Edata(i),... 'dictsize', SMALL.Problem.p,... 'iternum', 100,... 'maxatoms', 10); % Learn the dictionary SMALL.DL(i) = SMALL_learn(SMALL.Problem, SMALL.DL(i)); % 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(i).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='SPAMS'; SMALL.solver(1).name='mexLasso'; SMALL.solver(1).param=struct('lambda', 2, 'pos', 1, 'mode', 2); %Represent Training set in the learned dictionary 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. AMT_res(i) = AMT_analysis(SMALL.Problem, SMALL.solver(1)); if AMT_res(i).TP>TPmax TPmax=AMT_res(i).TP; BLmidi=SMALL.solver(1).reconstructed.midi; max=i; end end %end of for loop %% % Plot results and save midi files figAMTbest=SMALL_AMT_plot(SMALL, AMT_res(max)); resFig=figure('Name', 'Automatic Music Transcription KSVD Error TEST'); subplot (3,1,1); plot(Edata(:), [AMT_res(:).TP], 'ro-'); title('True Positives vs Edata'); subplot (3,1,2); plot(Edata(:), [AMT_res(:).FN], 'ro-'); title('False Negatives vs Edata'); subplot (3,1,3); plot(Edata(:), [AMT_res(:).FP], 'ro-'); title('False Positives vs Edata'); FS=filesep; [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m')); cd([pathstr1,FS,'results']); [filename,pathname] = uiputfile({' *.mid;' },'Save midi'); if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end [filename,pathname] = uiputfile({' *.fig;' },'Save figure TP/FN/FP vs lambda'); if filename~=0 saveas(resFig, [pathname,FS,filename]);end [filename,pathname] = uiputfile({' *.fig;' },'Save BEST AMT figure'); if filename~=0 saveas(figAMTbest, [pathname,FS,filename]);end