comparison examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m @ 107:dab78a3598b6

changes to comments for couple of scripts
author Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk>
date Wed, 18 May 2011 11:50:12 +0100
parents cbf3521c25eb
children 8e660fd14774
comparison
equal deleted inserted replaced
85:fd1c32cda22c 107:dab78a3598b6
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 %
12 % This file contains an example of how SMALLbox can be used to test diferent 3 % This file contains an example of how SMALLbox can be used to test diferent
13 % dictionary learning techniques in Automatic Music Transcription problem. 4 % dictionary learning techniques in Automatic Music Transcription problem.
14 % It calls generateAMT_Learning_Problem that will let you to choose midi, 5 % 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 6 % 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 7 % converted to wave and original midi file will be used for comparison with
17 % results of dictionary learning and reconstruction. 8 % results of dictionary learning and reconstruction.
18 % The function will generarte the Problem structure that is used to learn 9 % The function will generarte the Problem structure that is used to learn
19 % Problem.p notes spectrograms from training set Problem.b using 10 % Problem.p notes spectrograms from training set Problem.b using
20 % dictionary learning technique defined in DL structure. 11 % dictionary learning technique defined in DL structure.
12
13 %
14 % Centre for Digital Music, Queen Mary, University of London.
15 % This file copyright 2010 Ivan Damnjanovic.
16 %
17 % This program is free software; you can redistribute it and/or
18 % modify it under the terms of the GNU General Public License as
19 % published by the Free Software Foundation; either version 2 of the
20 % License, or (at your option) any later version. See the file
21 % COPYING included with this distribution for more information.
21 % 22 %
22 %% 23 %%
23 24
24 clear; 25 clear;
25 26