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

changes to comments for couple of scripts

 %%  DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
-%
-%   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.
 %%
 
 clear;
 
 

 
 SMALL.DL(1).toolbox = 'KSVD';
 SMALL.DL(1).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 3
+%   every frame in the training set can be represented with maximum Tdata
 %   dictionary elements. Type help ksvd in MATLAB prompt for more options.
 
 SMALL.DL(1).param=struct(...
-    'Tdata', 10,...
+    'Tdata', 3,...
     'dictsize', SMALL.Problem.p,...
-    'iternum', 100);
+    'iternum', 50);
 
 % Learn the dictionary
 
 SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
 

 end
 
 
 %%
 
-% %   Here we solve the same problem using non-negative sparse coding with 
-% %   SPAMS online dictionary learning (Julien Mairal 2009)
-% %
-% 
-% %   Initialising Dictionary structure
-% %   Setting Dictionary structure fields (toolbox, name, param, D and time) 
-% %   to zero values
-% 
-% SMALL.DL(2)=SMALL_init_DL();
-% 
-% 
-% %   Defining fields needed for dictionary learning
-% 
-% SMALL.DL(2).toolbox = 'SPAMS';
-% SMALL.DL(2).name = 'mexTrainDL';
-% 
-% %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
-% 
-% SMALL.DL(2).param=struct(...
-%     'K', SMALL.Problem.p,...
-%     'lambda', 3,...
-%     'iter', 300,...
-%     'posAlpha', 1,...
-%     'posD', 1,...
-%     'whiten', 0,...
-%     'mode', 2);
-% 
-% %   Learn the dictionary
-% 
-% SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
-% 
-% %   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(2).D;
-% SMALL.Problem.reconstruct=@(x) SMALL_midiGenerate(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(2)=SMALL_init_solver;
-% 
-% %   Defining the parameters needed for sparse representation
-% 
-% SMALL.solver(2).toolbox='SPAMS';
-% SMALL.solver(2).name='mexLasso';
-% 
-% %   Here we use mexLasso mode=2, with lambda=3, lambda2=0 and positivity
-% %   constrain (type 'help mexLasso' for more information about modes):
-% %   
-% %   min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2
-% 
-% SMALL.solver(2).param=struct('lambda', 3, 'pos', 1, 'mode', 2);
-% 
-% %   Call SMALL_soolve to represent the signal in the given dictionary. 
-% %   As a final command SMALL_solve will call above defined reconstruction
-% %   function to reconstruct the training set (Problem.b) in the learned 
-% %   dictionary (Problem.A)
-% 
-% SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
-% 
-% %%
-% %   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.
-% 
-% if ~isempty(SMALL.Problem.notesOriginal)
-%     AMT_res(2) = AMT_analysis(SMALL.Problem, SMALL.solver(2));
-% end
+%   Here we solve the same problem using non-negative sparse coding with 
+%   SPAMS online dictionary learning (Julien Mairal 2009)
+%
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time) 
+%   to zero values
+
+SMALL.DL(2)=SMALL_init_DL();
+
+
+%   Defining fields needed for dictionary learning
+
+SMALL.DL(2).toolbox = 'SPAMS';
+SMALL.DL(2).name = 'mexTrainDL';
+
+%   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+
+SMALL.DL(2).param=struct(...
+    'K', SMALL.Problem.p,...
+    'lambda', 3,...
+    'iter', 300,...
+    'posAlpha', 1,...
+    'posD', 1,...
+    'whiten', 0,...
+    'mode', 2);
+
+%   Learn the dictionary
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   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(2).D;
+SMALL.Problem.reconstruct=@(x) SMALL_midiGenerate(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(2)=SMALL_init_solver;
+
+%   Defining the parameters needed for sparse representation
+
+SMALL.solver(2).toolbox='SPAMS';
+SMALL.solver(2).name='mexLasso';
+
+%   Here we use mexLasso mode=2, with lambda=3, lambda2=0 and positivity
+%   constrain (type 'help mexLasso' for more information about modes):
+%   
+%   min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2
+
+SMALL.solver(2).param=struct('lambda', 3, 'pos', 1, 'mode', 2);
+
+%   Call SMALL_soolve to represent the signal in the given dictionary. 
+%   As a final command SMALL_solve will call above defined reconstruction
+%   function to reconstruct the training set (Problem.b) in the learned 
+%   dictionary (Problem.A)
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+%%
+%   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.
+
+if ~isempty(SMALL.Problem.notesOriginal)
+    AMT_res(2) = AMT_analysis(SMALL.Problem, SMALL.solver(2));
+end
 
 %%
 % Plot results and save midi files
 
 if ~isempty(SMALL.Problem.notesOriginal)