comparison examples/Automatic Music Transcription/SMALL_AMT_DL_test.m @ 25:cbf3521c25eb

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 %%  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.
 %
-%   Ivan Damnjanovic 2010
 %%
 
 clear;
 
 

 %   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
 %   dictionary elements. Type help ksvd in MATLAB prompt for more options.
 
 SMALL.DL(1).param=struct(...
-    'Tdata', 3,...
+    'Tdata', 10,...
     'dictsize', SMALL.Problem.p,...
     'iternum', 100);
 
 % Learn the dictionary
 

 
 SMALL.solver(1)=SMALL_init_solver;
 
 %   Defining the parameters needed for sparse representation
 
-SMALL.solver(1).toolbox='SPAMS';
-SMALL.solver(1).name='mexLasso';
+SMALL.solver(1).toolbox='SMALL';
+SMALL.solver(1).name='SMALL_cgp';
 
 %   Here we use mexLasso mode=2, with lambda=2, 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(1).param=struct(...
-    'lambda', 2,...
-    'pos', 1,...
-    'mode', 2);
+SMALL.solver(1).param='20, 1e-2';
+% struct(...
+%     'lambda', 2,...
+%     '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)

     AMT_res(1) = AMT_analysis(SMALL.Problem, SMALL.solver(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)