Mercurial > hg > smallbox
view Problems/generateAudioDenoiseProblem.m @ 39:8f734534839a
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author | idamnjanovic |
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date | Mon, 14 Mar 2011 15:35:01 +0000 |
parents | 0211faef9add |
children | 8e660fd14774 |
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function data=generateAudioDenoiseProblem(au, trainnum, blocksize, dictsize, overlap, sigma, gain, maxval, initdict); %%% Audio Denoising Problem % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2010 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. % % % % generateAudioDenoiseProblem is part of the SMALLbox and generate a % problem for comaprison of Dictionary Learning/Sparse Representation % techniques in audio denoising scenario. It is based on KSVD image % denoise demo by Ron Rubinstein (see bellow). % The fuction takes as an optional input % au - audio samples to be denoised % trainnum - number of frames for training % blocksize - 1D frame size (eg 512) % dictsize - number of atoms to be trained % overlap - ammount of overlaping frames between 0 and 1 % disp(' '); disp(' ********** Denoising Problem **********'); disp(' '); disp(' This function reads an audio, adds random Gaussian noise,'); disp(' that can be later denoised by using dictionary learning techniques.'); disp(' '); FS=filesep; if ~ exist( 'sigma', 'var' ) || isempty(sigma), sigma = 26.74; end if ~ exist( 'gain', 'var' ) || isempty(gain), gain = 1.15; end if ~ exist( 'initdict', 'var' ) || isempty(initdict), initdict = 'odct'; end if ~ exist( 'overlap', 'var' ) || isempty(overlap), overlap = 15/16; end %% prompt user for wav file %% %ask for file name TMPpath=pwd; if ~ exist( 'au', 'var' ) || isempty(au) [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m')); cd([pathstr1,FS,'data',FS,'audio',FS,'wav']); [filename,pathname] = uigetfile({'*.wav;'},'Select a wav file'); [pathstr, name, ext, versn] = fileparts(filename); data.name=name; au = wavread(filename); au = mean(au,2); % turn it into mono. end; if ~ exist( 'maxval', 'var' ) || isempty(maxval), maxval = max(au); end %% generate noisy audio %% disp(' '); disp('Generating noisy audio...'); sigma = max(au)/10^(sigma/20); n = randn(size(au)) .* sigma; aunoise = au + n;% here we can load noise audio if available % for example: wavread('icassp06_x.wav');% %% set parameters %% x = aunoise; if ~ exist( 'blocksize', 'var' ) || isempty(blocksize),blocksize = 512;end if ~ exist( 'dictsize', 'var' ) || isempty(dictsize), dictsize = 2048;end if ~ exist( 'trainnum', 'var' ) || isempty(trainnum),trainnum = (size(x,1)-blocksize+1);end p=1; % % msgdelta = 5; % % verbose = 't'; % if (msgdelta <= 0) % verbose=''; % msgdelta = -1; % end % % % % initial dictionary % % if (strcmpi(initdict,'odct')) initdict = odctndict(blocksize,dictsize,p); elseif (strcmpi(initdict,'data')) clear initdict; % causes initialization using random examples else error('Invalid initial dictionary specified.'); end if exist( 'initdict', 'var' ) initdict = initdict(:,1:dictsize); end % noise mode % % if (isfield(params,'noisemode')) % switch lower(params.noisemode) % case 'psnr' % sigma = maxval / 10^(params.psnr/20); % case 'sigma' % sigma = params.sigma; % otherwise % error('Invalid noise mode specified'); % end % elseif (isfield(params,'sigma')) % sigma = params.sigma; % elseif (isfield(params,'psnr')) % sigma = maxval / 10^(params.psnr/20); % else % error('Noise strength not specified'); % end % params.Edata = sqrt(prod(blocksize)) * sigma * gain; % target error for omp % params.codemode = 'error'; % % params.sigma = sigma; % params.noisemode = 'sigma'; % % % % make sure test data is not present in params % if (isfield(params,'testdata')) % params = rmfield(params,'testdata'); % end %%%% create training data %%% X = buffer( x(1:trainnum),blocksize, overlap*blocksize); % remove dc in blocks to conserve memory % % bsize = 2000; % for i = 1:bsize:size(X,2) % blockids = i : min(i+bsize-1,size(X,2)); % X(:,blockids) = remove_dc(X(:,blockids),'columns'); % end data.Original = au; data.Noisy = aunoise; data.b = X; data.m = size(X,1); data.n = size(X,2); data.p = dictsize; data.blocksize=blocksize; data.sigma = sigma; data.gain = gain; data.maxval = maxval; data.initdict= initdict; data.signalDim=1; cd(TMPpath);