diff examples/Automatic Music Transcription/SMALL_AMT_DL_test.m @ 6:f72603404233

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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Mon Mar 22 10:45:01 2010 +0000
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+%%  DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
+%   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;
+
+
+%   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', 3,...
+    '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='SPAMS';
+SMALL.solver(1).name='mexLasso';
+
+%   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);
+
+%   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
+
+
+