comparison examples/MajorizationMinimization tests/SMALL_AudioDenoise_DL_test_KSVDvsSPAMS.m @ 161:f42aa8bcb82f ivand_dev

debug and clean the SMALLbox Problems code

+%% DICTIONARY LEARNING FOR AUDIO DENOISING 
+% This file contains an example of how SMALLbox can be used to test different
+% dictionary learning techniques in Audio Denoising problem.
+% It calls generateAudioDenoiseProblem that will let you to choose audio file,
+% add noise and use noisy audio to generate training set for dictionary
+% learning.
+%   
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 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.
+%   
+%%
+
+clear;
+
+% Defining Audio Denoising Problem as Dictionary Learning
+% Problem
+
+SMALL.Problem = generateAudioDenoiseProblem('male01_8kHz',0.1,512,1/128,'','','',4);
+
+%% 
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(1)=SMALL_init_solver('MMbox', 'mm1', '', 1);
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(1).param=struct(...
+    'lambda', 0.2,...
+    'epsilon', 3*10^-4,...
+    'iternum',10); 
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(1)=SMALL_init_DL('MMbox', 'MM_cn', '', 1);
+
+
+%   Defining the parameters for MOD
+%   In this example we are learning 256 atoms in 20 iterations, so that
+%   every patch in the training set can be represented with target error in
+%   L2-norm (EData)
+%   Type help ksvd in MATLAB prompt for more options.
+
+
+SMALL.DL(1).param=struct(...
+    'solver', SMALL.solver(1),...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'iternum', 20,...
+    'iterDictUpdate', 10,...
+    'epsDictUpdate', 10^-7,...
+    'cvset',0,...
+    'show_dict', 0);
+
+%   Learn the dictionary
+
+SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
+
+%   Set SMALL.Problem.A dictionary
+%   (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) AudioDenoise_reconstruct(x, SMALL.Problem);
+%   Denoising the image - find the sparse solution in the learned
+%   dictionary for all patches in the image and the end it uses
+%   reconstruction function to reconstruct the patches and put them into a
+%   denoised image
+
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+
+%%
+%%
+% %  sparse coding using SPAMS online dictionary learning
+% 
+
+SMALL.DL(2)=SMALL_init_DL();
+SMALL.DL(2).toolbox = 'SPAMS';
+SMALL.DL(2).name = 'mexTrainDL';
+SMALL.DL(2).param=struct('D', SMALL.Problem.initdict, 'K', SMALL.Problem.p, 'lambda', 0.2, 'iter', 200, 'mode', 3, 'modeD', 0);
+
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+% Defining Reconstruction function
+
+SMALL.Problem.A = SMALL.DL(2).D;
+
+
+%%
+% Initialising solver structure 
+% Setting toolbox, name, param, solution, reconstructed and time to zero values
+
+SMALL.solver(2)=SMALL_init_solver;
+
+% Defining the parameters needed for sparse representation
+
+SMALL.solver(2).toolbox='ompbox';
+SMALL.solver(2).name='omp2';
+SMALL.solver(2).param=struct(...
+    'epsilon',0.2,...
+    'maxatoms', 128); 
+% Represent Training set in the learned dictionary 
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+
+
+
+%%
+% Plot results and save midi files
+
+% show results %
+
+
+SMALL_AudioDeNoiseResult(SMALL);
+