view examples/Automatic Music Transcription/SMALL_AMT_DL_test.m @ 38:84b7c44e54c4

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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.
%
%%

clear;


%   Defining Automatic Transcription of Piano tune as Dictionary Learning
%   Problem

SMALL.Problem = generateAMT_Learning_Problem();

%%
%   Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in
%   SMALL.Problem.p) using sparsity constrain only

%   Initialising Dictionary structure
%   Setting Dictionary structure fields (toolbox, name, param, D and time) 
%   to zero values

SMALL.DL(1)=SMALL_init_DL();

%   Defining fields needed for dictionary learning

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
%   dictionary elements. Type help ksvd in MATLAB prompt for more options.

SMALL.DL(1).param=struct(...
    'Tdata', 10,...
    'dictsize', SMALL.Problem.p,...
    'iternum', 100);

% Learn the dictionary

SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));

%   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(1).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(1)=SMALL_init_solver;

%   Defining the parameters needed for sparse representation

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='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)

SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));

%%
%   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(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

%%
% Plot results and save midi files

if ~isempty(SMALL.Problem.notesOriginal)
    figAMT = SMALL_AMT_plot(SMALL, AMT_res);
else
    figAMT = figure('Name', 'Automatic Music Transcription KSVD vs SPAMS');
    subplot(2,1,1); plot(SMALL.solver(1).reconstructed.notes(:,5), SMALL.solver(1).reconstructed.notes(:,3), 'kd ');
            title (sprintf('%s dictionary in %.2f s', SMALL.DL(1).name, SMALL.DL(1).time));
                xlabel('Time');
                    ylabel('Note Number');
    subplot(2,1,2); plot(SMALL.solver(2).reconstructed.notes(:,5), SMALL.solver(2).reconstructed.notes(:,3), 'b* ');
            title (sprintf('%s dictionary in %.2f s', SMALL.DL(2).name, SMALL.DL(2).time));
                xlabel('Time');
                    ylabel('Note Number');
end

FS=filesep;

[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
cd([pathstr1,FS,'results']);

[filename,pathname] = uiputfile({' *.mid;' },'Save KSVD result midi');
if filename~=0 writemidi(SMALL.solver(1).reconstructed.midi, [pathname,FS,filename]);end

[filename,pathname] = uiputfile({' *.mid;' },'Save SPAMS result midi');
if filename~=0 writemidi(SMALL.solver(2).reconstructed.midi, [pathname,FS,filename]);end

[filename,pathname] = uiputfile({' *.fig;' },'Save KSVD vs SPAMS AMT figure');
if filename~=0 saveas(figAMT, [pathname,FS,filename]);end