ivan@128: %%  Dictionary Learning for Automatic Music Transcription - KSVD residual 
ivan@128: %%  error test
ivan@128: %   
ivan@128: %   *WARNING!* You should have SPAMS in your search path in order for this
ivan@128: %   script to work.Due to licensing issues SPAMS can not be automatically 
ivan@128: %   provided in SMALLbox (http://www.di.ens.fr/willow/SPAMS/downloads.html).
idamnjanovic@25: %
idamnjanovic@6: %   This file contains an example of how SMALLbox can be used to test diferent
idamnjanovic@6: %   dictionary learning techniques in Automatic Music Transcription problem.
idamnjanovic@6: %   It calls generateAMT_Learning_Problem that will let you to choose midi,
idamnjanovic@6: %   wave or mat file to be transcribe. If file is midi it will be first
idamnjanovic@6: %   converted to wave and original midi file will be used for comparison with
idamnjanovic@6: %   results of dictionary learning and reconstruction.
idamnjanovic@6: %   The function will generarte the Problem structure that is used to learn
idamnjanovic@6: %   Problem.p notes spectrograms from training set Problem.b using
idamnjanovic@6: %   dictionary learning technique defined in DL structure.
idamnjanovic@6: %
idamnjanovic@25: 
ivan@128: %
ivan@128: %   Centre for Digital Music, Queen Mary, University of London.
ivan@128: %   This file copyright 2010 Ivan Damnjanovic.
ivan@128: %
ivan@128: %   This program is free software; you can redistribute it and/or
ivan@128: %   modify it under the terms of the GNU General Public License as
ivan@128: %   published by the Free Software Foundation; either version 2 of the
ivan@128: %   License, or (at your option) any later version.  See the file
ivan@128: %   COPYING included with this distribution for more information.
idamnjanovic@6: %%
idamnjanovic@6: 
idamnjanovic@6: clear;
idamnjanovic@6: 
idamnjanovic@6: 
idamnjanovic@6: % Defining Automatic Transcription of Piano tune as Dictionary Learning
idamnjanovic@6: % Problem
idamnjanovic@6: 
ivan@161: SMALL.Problem = generateAMTProblem();
idamnjanovic@6: TPmax=0;
idamnjanovic@6: for i=1:5
idamnjanovic@6:     %%
idamnjanovic@6:     %   Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in
idamnjanovic@6:     %   SMALL.Problem.p) using sparsity constrain only
idamnjanovic@6:     
idamnjanovic@6:     %   Initialising Dictionary structure
idamnjanovic@6:     %   Setting Dictionary structure fields (toolbox, name, param, D and time)
idamnjanovic@6:     %   to zero values
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(i)=SMALL_init_DL(i);
idamnjanovic@6:     
idamnjanovic@6:     % Defining the parameters needed for dictionary learning
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(i).toolbox = 'KSVD';
idamnjanovic@6:     SMALL.DL(i).name = 'ksvd';
idamnjanovic@6:     
idamnjanovic@6:     %   Defining the parameters for KSVD
idamnjanovic@6:     %   In this example we are learning 88 atoms in 100 iterations, so that
idamnjanovic@6:     %   every frame in the training set can be represented with maximum 10
idamnjanovic@6:     %   dictionary elements. However, our aim here is to show how individual
idamnjanovic@6:     %   parameters can be ested in the AMT problem. We test five different
idamnjanovic@6:     %   values for residual error (Edata) in KSVD algorithm.
idamnjanovic@6:     %   Type help ksvd in MATLAB prompt for more options.
idamnjanovic@6:     
idamnjanovic@6:     Edata(i)=8+i*2;
idamnjanovic@6:     SMALL.DL(i).param=struct(...
idamnjanovic@6:         'Edata', Edata(i),...
idamnjanovic@6:         'dictsize', SMALL.Problem.p,...
idamnjanovic@6:         'iternum', 100,...
idamnjanovic@6:         'maxatoms', 10);
idamnjanovic@6:     
idamnjanovic@6:     % Learn the dictionary
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(i) = SMALL_learn(SMALL.Problem, SMALL.DL(i));
idamnjanovic@6:     
idamnjanovic@6:     %   Set SMALL.Problem.A dictionary and reconstruction function
idamnjanovic@6:     %   (backward compatiblity with SPARCO: solver structure communicate
idamnjanovic@6:     %   only with Problem structure, ie no direct communication between DL and
idamnjanovic@6:     %   solver structures)
idamnjanovic@6:     
idamnjanovic@6:     SMALL.Problem.A = SMALL.DL(i).D;
ivan@161:     SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem);
idamnjanovic@6:     
idamnjanovic@6:     %%
idamnjanovic@6:     %   Initialising solver structure
idamnjanovic@6:     %   Setting solver structure fields (toolbox, name, param, solution,
idamnjanovic@6:     %   reconstructed and time) to zero values
idamnjanovic@6:     %   As an example, SPAMS (Julien Mairal 2009) implementation of LARS
idamnjanovic@6:     %   algorithm is used for representation of training set in the learned
idamnjanovic@6:     %   dictionary.
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1)=SMALL_init_solver;
idamnjanovic@6:     
idamnjanovic@6:     % Defining the parameters needed for sparse representation
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1).toolbox='SPAMS';
idamnjanovic@6:     SMALL.solver(1).name='mexLasso';
idamnjanovic@6:     SMALL.solver(1).param=struct('lambda', 2, 'pos', 1, 'mode', 2);
idamnjanovic@6:     
idamnjanovic@6:     %Represent Training set in the learned dictionary
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
idamnjanovic@6:     
idamnjanovic@6:     %%
idamnjanovic@6:     %   Analysis of the result of automatic music transcription. If groundtruth
idamnjanovic@6:     %   exists, we can compare transcribed notes and original and get usual
idamnjanovic@6:     %   True Positives, False Positives and False Negatives measures.
idamnjanovic@6:     
idamnjanovic@6:     AMT_res(i) = AMT_analysis(SMALL.Problem, SMALL.solver(1));
idamnjanovic@6:     
idamnjanovic@6:     if AMT_res(i).TP>TPmax
idamnjanovic@6:         TPmax=AMT_res(i).TP;
idamnjanovic@6:         BLmidi=SMALL.solver(1).reconstructed.midi;
idamnjanovic@6:         max=i;
idamnjanovic@6:     end
idamnjanovic@6:     
idamnjanovic@6: end %end of for loop
idamnjanovic@6: 
idamnjanovic@6: %%
idamnjanovic@6: % Plot results and save midi files
idamnjanovic@6: 
idamnjanovic@6: figAMTbest=SMALL_AMT_plot(SMALL, AMT_res(max));
idamnjanovic@6: 
idamnjanovic@6: resFig=figure('Name', 'Automatic Music Transcription KSVD Error TEST');
idamnjanovic@6: 
idamnjanovic@6: subplot (3,1,1); plot(Edata(:), [AMT_res(:).TP], 'ro-');
idamnjanovic@6: title('True Positives vs Edata');
idamnjanovic@6: 
idamnjanovic@6: subplot (3,1,2); plot(Edata(:), [AMT_res(:).FN], 'ro-');
idamnjanovic@6: title('False Negatives vs Edata');
idamnjanovic@6: 
idamnjanovic@6: subplot (3,1,3); plot(Edata(:), [AMT_res(:).FP], 'ro-');
idamnjanovic@6: title('False Positives vs Edata');
idamnjanovic@6: 
idamnjanovic@6: FS=filesep;
luis@186: [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
idamnjanovic@6: cd([pathstr1,FS,'results']);
idamnjanovic@6: [filename,pathname] = uiputfile({' *.mid;' },'Save midi');
idamnjanovic@6: if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
idamnjanovic@6: [filename,pathname] = uiputfile({' *.fig;' },'Save figure TP/FN/FP vs lambda');
idamnjanovic@6: if filename~=0 saveas(resFig, [pathname,FS,filename]);end
idamnjanovic@6: 
idamnjanovic@6: [filename,pathname] = uiputfile({' *.fig;' },'Save BEST AMT figure');
idamnjanovic@6: if filename~=0 saveas(figAMTbest, [pathname,FS,filename]);end
idamnjanovic@6: