Mercurial > hg > smallbox
comparison examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m @ 25:cbf3521c25eb
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author | idamnjanovic |
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date | Tue, 27 Apr 2010 13:33:13 +0000 |
parents | f72603404233 |
children | dab78a3598b6 |
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24:fc395272d53e | 25:cbf3521c25eb |
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1 %% DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1 | 1 %% DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1 |
2 % | |
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 % | |
2 % This file contains an example of how SMALLbox can be used to test diferent | 12 % This file contains an example of how SMALLbox can be used to test diferent |
3 % dictionary learning techniques in Automatic Music Transcription problem. | 13 % dictionary learning techniques in Automatic Music Transcription problem. |
4 % It calls generateAMT_Learning_Problem that will let you to choose midi, | 14 % It calls generateAMT_Learning_Problem that will let you to choose midi, |
5 % wave or mat file to be transcribe. If file is midi it will be first | 15 % wave or mat file to be transcribe. If file is midi it will be first |
6 % converted to wave and original midi file will be used for comparison with | 16 % converted to wave and original midi file will be used for comparison with |
7 % results of dictionary learning and reconstruction. | 17 % results of dictionary learning and reconstruction. |
8 % The function will generarte the Problem structure that is used to learn | 18 % The function will generarte the Problem structure that is used to learn |
9 % Problem.p notes spectrograms from training set Problem.b using | 19 % Problem.p notes spectrograms from training set Problem.b using |
10 % dictionary learning technique defined in DL structure. | 20 % dictionary learning technique defined in DL structure. |
11 % | 21 % |
12 % Ivan Damnjanovic 2010 | |
13 %% | 22 %% |
14 | 23 |
15 clear; | 24 clear; |
16 | 25 |
17 | 26 |