diff Problems/generateAudioDenoiseProblem.m @ 10:207a6ae9a76f version1.0

(none)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/generateAudioDenoiseProblem.m	Mon Mar 22 15:06:25 2010 +0000
@@ -0,0 +1,159 @@
+function data=generateAudioDenoiseProblem(au, trainnum, blocksize, dictsize, overlap, sigma, gain, maxval, initdict);
+
+%   Ivan Damnjanovic 2010 
+% 
+%
+%   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
+%   Ron Rubinstein
+%   Computer Science Department
+%   Technion, Haifa 32000 Israel
+%   ronrubin@cs
+%
+%   August 2009
+
+
+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);
+