comparison examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m @ 128:8e660fd14774 ivand_dev

Feature 186
author Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk>
date Mon, 13 Jun 2011 14:55:45 +0100
parents cbf3521c25eb
children f42aa8bcb82f
comparison
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126:db5a7fe1a404 128:8e660fd14774
1 %% DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1 1 %% Dictionary Learning for Automatic Music Transcription - KSVD sparsity
2 %% test
3 %
4 % *WARNING!* You should have SPAMS in your search path in order for this
5 % script to work.Due to licensing issues SPAMS can not be automatically
6 % provided in SMALLbox (http://www.di.ens.fr/willow/SPAMS/downloads.html).
2 % 7 %
3 % Centre for Digital Music, Queen Mary, University of London.
4 % This file copyright 2010 Ivan Damnjanovic.
5 %
6 % This program is free software; you can redistribute it and/or
7 % modify it under the terms of the GNU General Public License as
8 % published by the Free Software Foundation; either version 2 of the
9 % License, or (at your option) any later version. See the file
10 % COPYING included with this distribution for more information.
11 %
12 % This file contains an example of how SMALLbox can be used to test diferent 8 % This file contains an example of how SMALLbox can be used to test diferent
13 % dictionary learning techniques in Automatic Music Transcription problem. 9 % dictionary learning techniques in Automatic Music Transcription problem.
14 % It calls generateAMT_Learning_Problem that will let you to choose midi, 10 % It calls generateAMT_Learning_Problem that will let you to choose midi,
15 % wave or mat file to be transcribe. If file is midi it will be first 11 % wave or mat file to be transcribe. If file is midi it will be first
16 % converted to wave and original midi file will be used for comparison with 12 % converted to wave and original midi file will be used for comparison with
17 % results of dictionary learning and reconstruction. 13 % results of dictionary learning and reconstruction.
18 % The function will generarte the Problem structure that is used to learn 14 % The function will generarte the Problem structure that is used to learn
19 % Problem.p notes spectrograms from training set Problem.b using 15 % Problem.p notes spectrograms from training set Problem.b using
20 % dictionary learning technique defined in DL structure. 16 % dictionary learning technique defined in DL structure.
21 % 17 %
18
19 %
20 % Centre for Digital Music, Queen Mary, University of London.
21 % This file copyright 2010 Ivan Damnjanovic.
22 %
23 % This program is free software; you can redistribute it and/or
24 % modify it under the terms of the GNU General Public License as
25 % published by the Free Software Foundation; either version 2 of the
26 % License, or (at your option) any later version. See the file
27 % COPYING included with this distribution for more information.
22 %% 28 %%
23 29
24 clear; 30 clear;
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26 32