idamnjanovic@10: function data=generateAudioDenoiseProblem(au, trainnum, blocksize, dictsize, overlap, sigma, gain, maxval, initdict);
ivan@128: %%  Audio Denoising Problem - needs revision, not yet finalised
idamnjanovic@10: %
idamnjanovic@10: %   generateAudioDenoiseProblem is part of the SMALLbox and generate a
idamnjanovic@10: %   problem for comaprison of Dictionary Learning/Sparse Representation
idamnjanovic@10: %   techniques in audio denoising scenario. It is based on KSVD image
idamnjanovic@10: %   denoise demo by Ron Rubinstein (see bellow).
idamnjanovic@10: %   The fuction takes as an optional input 
idamnjanovic@10: %       au - audio samples to be denoised
idamnjanovic@10: %       trainnum - number of frames for training 
idamnjanovic@10: %       blocksize - 1D frame size (eg 512)
idamnjanovic@10: %       dictsize - number of atoms to be trained
idamnjanovic@10: %       overlap - ammount of overlaping frames between 0 and 1
idamnjanovic@21: %   
idamnjanovic@10: 
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.
ivan@128: %%  
idamnjanovic@10: disp(' ');
idamnjanovic@10: disp('  **********  Denoising Problem  **********');
idamnjanovic@10: disp(' ');
idamnjanovic@10: disp('  This function reads an audio, adds random Gaussian noise,');
idamnjanovic@10: disp('  that can be later denoised by using dictionary learning techniques.');
idamnjanovic@10: disp(' ');
idamnjanovic@10: 
idamnjanovic@10: FS=filesep;
idamnjanovic@10: if ~ exist( 'sigma', 'var' ) || isempty(sigma), sigma = 26.74; end
idamnjanovic@10: if ~ exist( 'gain', 'var' ) || isempty(gain), gain = 1.15; end
idamnjanovic@10: 
idamnjanovic@10: if ~ exist( 'initdict', 'var' ) || isempty(initdict), initdict = 'odct'; end
idamnjanovic@10: if ~ exist( 'overlap', 'var' ) || isempty(overlap), overlap = 15/16; end
idamnjanovic@10: %% prompt user for wav file %%
idamnjanovic@10: %ask for file name
idamnjanovic@10: 
idamnjanovic@10: TMPpath=pwd;
idamnjanovic@10: if ~ exist( 'au', 'var' ) || isempty(au)
idamnjanovic@10:     [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
idamnjanovic@10:     cd([pathstr1,FS,'data',FS,'audio',FS,'wav']);
idamnjanovic@10:     [filename,pathname] = uigetfile({'*.wav;'},'Select a wav file');
idamnjanovic@10:     [pathstr, name, ext, versn] = fileparts(filename);
idamnjanovic@10:     data.name=name;
idamnjanovic@10:     
idamnjanovic@10:     au = wavread(filename);
idamnjanovic@10:     au = mean(au,2); % turn it into mono.
idamnjanovic@10: end;
idamnjanovic@10: if ~ exist( 'maxval', 'var' ) || isempty(maxval), maxval = max(au); end
idamnjanovic@10: 
idamnjanovic@10: %% generate noisy audio %%
idamnjanovic@10: 
idamnjanovic@10: disp(' ');
idamnjanovic@10: disp('Generating noisy audio...');
idamnjanovic@10: sigma = max(au)/10^(sigma/20); 
idamnjanovic@10: n = randn(size(au)) .* sigma;
idamnjanovic@10: aunoise = au + n;%  here we can load noise audio if available 
idamnjanovic@10:                  %  for example: wavread('icassp06_x.wav');%
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: %% set parameters %%
idamnjanovic@10: 
idamnjanovic@10: x = aunoise;
idamnjanovic@10: if ~ exist( 'blocksize', 'var' ) || isempty(blocksize),blocksize = 512;end
idamnjanovic@10: if ~ exist( 'dictsize', 'var' ) || isempty(dictsize), dictsize = 2048;end
idamnjanovic@10: 
idamnjanovic@10: if ~ exist( 'trainnum', 'var' ) || isempty(trainnum),trainnum = (size(x,1)-blocksize+1);end
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: p=1;
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: % 
idamnjanovic@10: % msgdelta = 5;
idamnjanovic@10: % 
idamnjanovic@10: % verbose = 't';
idamnjanovic@10: % if (msgdelta <= 0)
idamnjanovic@10: %   verbose='';
idamnjanovic@10: %   msgdelta = -1;
idamnjanovic@10: % end
idamnjanovic@10: % 
idamnjanovic@10: % 
idamnjanovic@10: % % initial dictionary %
idamnjanovic@10: % 
idamnjanovic@10: if (strcmpi(initdict,'odct'))
idamnjanovic@10:     initdict = odctndict(blocksize,dictsize,p);
idamnjanovic@10: elseif (strcmpi(initdict,'data'))
idamnjanovic@10:     clear initdict;    % causes initialization using random examples
idamnjanovic@10: else
idamnjanovic@10:     error('Invalid initial dictionary specified.');
idamnjanovic@10: end
idamnjanovic@10: 
idamnjanovic@10: if exist( 'initdict', 'var' ) 
idamnjanovic@10:   initdict = initdict(:,1:dictsize);
idamnjanovic@10: end
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: % noise mode %
idamnjanovic@10: % if (isfield(params,'noisemode'))
idamnjanovic@10: %   switch lower(params.noisemode)
idamnjanovic@10: %     case 'psnr'
idamnjanovic@10: %       sigma = maxval / 10^(params.psnr/20);
idamnjanovic@10: %     case 'sigma'
idamnjanovic@10: %       sigma = params.sigma;
idamnjanovic@10: %     otherwise
idamnjanovic@10: %       error('Invalid noise mode specified');
idamnjanovic@10: %   end
idamnjanovic@10: % elseif (isfield(params,'sigma'))
idamnjanovic@10: %   sigma = params.sigma;
idamnjanovic@10: % elseif (isfield(params,'psnr'))
idamnjanovic@10: %   sigma = maxval / 10^(params.psnr/20);
idamnjanovic@10: % else
idamnjanovic@10: %   error('Noise strength not specified');
idamnjanovic@10: % end
idamnjanovic@10: 
idamnjanovic@10: % params.Edata = sqrt(prod(blocksize)) * sigma * gain;   % target error for omp
idamnjanovic@10: % params.codemode = 'error';
idamnjanovic@10: % 
idamnjanovic@10: % params.sigma = sigma;
idamnjanovic@10: % params.noisemode = 'sigma';
idamnjanovic@10: % 
idamnjanovic@10: % 
idamnjanovic@10: % % make sure test data is not present in params
idamnjanovic@10: % if (isfield(params,'testdata'))
idamnjanovic@10: %   params = rmfield(params,'testdata');
idamnjanovic@10: % end
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: %%%% create training data %%%
idamnjanovic@10: 
idamnjanovic@10: 
idamnjanovic@10: X = buffer( x(1:trainnum),blocksize, overlap*blocksize);
idamnjanovic@10: 
idamnjanovic@10: % remove dc in blocks to conserve memory %
idamnjanovic@10: % bsize = 2000;
idamnjanovic@10: % for i = 1:bsize:size(X,2)
idamnjanovic@10: %   blockids = i : min(i+bsize-1,size(X,2));
idamnjanovic@10: %   X(:,blockids) = remove_dc(X(:,blockids),'columns');
idamnjanovic@10: % end
idamnjanovic@10: data.Original = au;
idamnjanovic@10: data.Noisy = aunoise;
idamnjanovic@10: data.b = X;
idamnjanovic@10: data.m = size(X,1);
idamnjanovic@10: data.n = size(X,2);
idamnjanovic@10: data.p = dictsize;
idamnjanovic@10: data.blocksize=blocksize;
idamnjanovic@10: data.sigma = sigma;
idamnjanovic@10: data.gain = gain;
idamnjanovic@10: data.maxval = maxval;
idamnjanovic@10: data.initdict= initdict;
idamnjanovic@10: data.signalDim=1;
idamnjanovic@10: cd(TMPpath);
idamnjanovic@10: