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

Feature 186

-%%  DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
+%%  Dictionary Learning for Automatic Music Transcription - KSVD sparsity
+%%  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).
 %
-%   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.
-%   
 %   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.
 %%
 
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