comparison examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m @ 6:f72603404233

(none)

+%% DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION
+%   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();
+TPmax=0;
+for i=1:5
+    %%
+    %   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(i)=SMALL_init_DL(i);
+    
+    % Defining the parameters needed for dictionary learning
+    
+    SMALL.DL(i).toolbox = 'KSVD';
+    SMALL.DL(i).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 10
+    %   dictionary elements. However, our aim here is to show how individual
+    %   parameters can be ested in the AMT problem. We test five different
+    %   values for residual error (Edata) in KSVD algorithm.
+    %   Type help ksvd in MATLAB prompt for more options.
+    
+    Edata(i)=8+i*2;
+    SMALL.DL(i).param=struct(...
+        'Edata', Edata(i),...
+        'dictsize', SMALL.Problem.p,...
+        'iternum', 100,...
+        'maxatoms', 10);
+    
+    % Learn the dictionary
+    
+    SMALL.DL(i) = SMALL_learn(SMALL.Problem, SMALL.DL(i));
+    
+    %   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(i).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';
+    SMALL.solver(1).param=struct('lambda', 2, 'pos', 1, 'mode', 2);
+    
+    %Represent Training set in the learned dictionary
+    
+    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.
+    
+    AMT_res(i) = AMT_analysis(SMALL.Problem, SMALL.solver(1));
+    
+    if AMT_res(i).TP>TPmax
+        TPmax=AMT_res(i).TP;
+        BLmidi=SMALL.solver(1).reconstructed.midi;
+        max=i;
+    end
+    
+end %end of for loop
+
+%%
+% Plot results and save midi files
+
+figAMTbest=SMALL_AMT_plot(SMALL, AMT_res(max));
+
+resFig=figure('Name', 'Automatic Music Transcription KSVD Error TEST');
+
+subplot (3,1,1); plot(Edata(:), [AMT_res(:).TP], 'ro-');
+title('True Positives vs Edata');
+
+subplot (3,1,2); plot(Edata(:), [AMT_res(:).FN], 'ro-');
+title('False Negatives vs Edata');
+
+subplot (3,1,3); plot(Edata(:), [AMT_res(:).FP], 'ro-');
+title('False Positives vs Edata');
+
+FS=filesep;
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'results']);
+[filename,pathname] = uiputfile({' *.mid;' },'Save midi');
+if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
+[filename,pathname] = uiputfile({' *.fig;' },'Save figure TP/FN/FP vs lambda');
+if filename~=0 saveas(resFig, [pathname,FS,filename]);end
+
+[filename,pathname] = uiputfile({' *.fig;' },'Save BEST AMT figure');
+if filename~=0 saveas(figAMTbest, [pathname,FS,filename]);end
+