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changeset 178:4ea4badb2266 danieleb

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added ramirez dl (to be completed) and MOCOD dictionary update
author Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk>
date Thu, 17 Nov 2011 11:22:17 +0000
parents 714fa7b8c1ad (current diff) 775caafd5651 (diff)
children 989b7d78e1c8
files .hgtags util/SMALL_solve.m
diffstat 34 files changed, 1186 insertions(+), 152 deletions(-) [+]
line wrap: on
line diff
--- a/.hgtags	Thu Nov 17 11:18:25 2011 +0000
+++ b/.hgtags	Thu Nov 17 11:22:17 2011 +0000
@@ -5,4 +5,4 @@
 19e0af5709140e163faaf9d8cf4b83a664be1edc release_1.5
 a4d0977d45956b96579a3ed83a4e6a1869ee6055 danieleb
 a4d0977d45956b96579a3ed83a4e6a1869ee6055 danieleb
-0000000000000000000000000000000000000000 danieleb
+0000000000000000000000000000000000000000 danieleb
\ No newline at end of file
--- a/DL/Majorization Minimization DL/wrapper_mm_solver.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/DL/Majorization Minimization DL/wrapper_mm_solver.m	Thu Nov 17 11:22:17 2011 +0000
@@ -33,7 +33,7 @@
 if isfield(param, 'to')
    to = param.to;
 else
-   to = .1+svds(A,1);
+   to = .1+(svds(A,1))^2;
 end
 
 % lambda - Lagrangian multiplier. (regulates shrinkage)
@@ -65,7 +65,7 @@
 if isfield(param, 'map')
     map = param.map;
 else
-    map = 1; 
+    map = 0; 
 end
 
 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/AMT_reconstruct.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,117 @@
+function reconstructed=AMT_reconstruct(V, Problem)
+%% Reconstruction of midi file from representation in the given dictionary
+%   
+%   SMALL_midiGenerate is a part of SMALLbox and can be use to reconstruct
+%   a midi file given representation of the training set (V) in the
+%   dictionary Problem.A.
+%   Output is reconstructed structure with two fields:
+%   -   reconstructed.notes - matrix with transcribed notes
+%   -   reconstructed.midi - midi representation of transcription
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
+%%
+U=Problem.A;    %   Dictionary used for representation
+fs=Problem.fs;  %   Sampling rate
+f=Problem.f;    %   vector of frequencies at wihch spectrogram is computed
+
+ts=(Problem.windowSize*(1-Problem.overlap))/fs; %size of an analysis frame in seconds
+
+%%
+% Components pitch estimation using modified SWIPE algorithm by Arthuro
+% Camacho
+% 
+% Columns of matrix U are spectrograms of the notes learned from the
+% training set. We are estimating pitches of these notes by also
+% restricting pitch values to the one of the 88 piano notes. 
+
+pitch=zeros(size(U,2),1);
+
+for i=1:size(U,2)
+    
+    pitch(i) = SMALL_swipe(U(:,i),fs, f, [27.50 8192], 1/12);
+    
+end
+
+%%
+% If some of columns of U have the same pitch, their contribution to the
+% score (matrix V) is summed.
+
+[Ps,idx]=sort(pitch);
+ndp=1;
+Pd(ndp)=Ps(1);
+Vnew(ndp,:)=V(idx(1),:);
+for i=2:88
+    if Ps(i)> Ps(i-1)
+        
+        ndp=ndp+1;
+        Vnew(ndp,:)=V(idx(i),:);
+        Pd(ndp)=Ps(i);
+        
+    else
+        Vnew(ndp,:)=Vnew(ndp,:)+V(idx(i),:);
+    end
+end
+%%
+% Generate midi matrix
+
+midx=0;
+for i=1:ndp
+    
+    %   Threshold for finding onsets and offsets of notes
+    
+    thr=mean(Vnew(i,:));%+std(Vnew(i,:));
+    
+    if(Pd(i)~=0)
+        for j=1:size(Vnew,2)
+            if Vnew(i,j)<thr
+                Vnew(i,j)=0;
+                if(j>1)
+                    if (Vnew(i,j-1)==1)
+                        try
+                            M(midx,6)=(j-1)*ts;
+                            if (M(midx,6)-M(midx,5))<2*ts
+                                midx=midx-1;
+                            end
+                        catch
+                            pause;
+                        end
+                    end
+                end
+            else
+                Vnew(i,j)=1;
+                if(j>1)
+                    if (Vnew(i,j-1)==0)
+                        midx=midx+1;
+                        M(midx,1)=1;
+                        M(midx,2)=1;
+                        M(midx,3)=69 +round( 12 *log2(Pd(i)/440));
+                        M(midx,4)=80;
+                        M(midx,5)=(j-1)*ts;
+                    end
+                else
+                    midx=midx+1;
+                    M(midx,1)=1;
+                    M(midx,2)=1;
+                    M(midx,3)=69 + round(12 *log2(Pd(i)/440));
+                    M(midx,4)=80;
+                    M(midx,5)=0;
+                end
+            end
+        end
+        if M(midx,6)==0
+            M(midx,6)=(j-1)*ts;
+        end
+    end
+end
+
+M=sortrows(M,5);
+reconstructed.notes=M;
+reconstructed.midi = matrix2midi(M);
--- a/Problems/AudioDeclipping_reconstruct.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/Problems/AudioDeclipping_reconstruct.m	Thu Nov 17 11:22:17 2011 +0000
@@ -1,8 +1,15 @@
-function reconstructed=AudioDeclipping_reconstruct(y, Problem, SparseDict)
+function reconstructed = AudioDeclipping_reconstruct(y, Problem)
 %%  Audio declipping Problem reconstruction function
 %   
 %   This reconstruction function is using sparse representation y 
 %   in dictionary Problem.A to reconstruct declipped audio.
+%   The output structure has following fields:
+%       audioAllSamples  - signal with all samples taken from reconstructed
+%                          signal
+%       audioOnlyClipped - only clipped samples are reconstructed,
+%                          others are taken from original signal
+%       snrAll           - psnr of whole signal
+%       snrMiss          - psnr of the reconstructed clipped samples
 %
 %   [1] I. Damnjanovic, M. E. P. Davies, and M. P. Plumbley "SMALLbox - an 
 %   evaluation framework for sparse representations and dictionary 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/AudioDenoise_reconstruct.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,53 @@
+function reconstructed=AudioDenoise_reconstruct(y, Problem)
+%%  Audio denoising Problem reconstruction function
+%   
+%   This reconstruction function is using sparse representation y 
+%   in dictionary Problem.A to reconstruct denoised audio.
+%   The output structre has following fields:
+%       audio   - denoised audio signal
+%       psnr    - psnr of the reconstructed audio signal
+%
+%   [1] I. Damnjanovic, M. E. P. Davies, and M. P. Plumbley "SMALLbox - an 
+%   evaluation framework for sparse representations and dictionary 
+%   learning algorithms," V. Vigneron et al. (Eds.): LVA/ICA 2010, 
+%   Springer-Verlag, Berlin, Germany, LNCS 6365, pp. 418-425
+
+%
+%   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.
+%%
+
+windowSize = Problem.windowSize;
+overlap = Problem.overlap;
+ws = Problem.ws(windowSize);
+wa = Problem.wa(windowSize);
+
+A = Problem.A;
+
+orig   = Problem.Original;
+noisy  = Problem.Noisy;
+
+
+% reconstruct audio frames
+
+xFrames = diag(ws)*(A*y);
+wNormFrames = (ws.*wa)'*ones(1,size(xFrames,2));
+
+%   overlap and add
+
+rec   = col2imstep(xFrames, size(noisy), [windowSize 1], [windowSize*overlap 1]);
+wNorm = col2imstep(wNormFrames, size(noisy), [windowSize 1], [windowSize*overlap 1]); 
+wNorm(find(wNorm==0)) = 1; 
+recN  = rec./wNorm;
+
+%% output structure image+psnr %%
+reconstructed.audio  = recN;
+reconstructed.psnr = 20*log10(sqrt(numel(orig)) / norm(orig - reconstructed.audio));
+
+end
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/ImageDenoise_reconstruct.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,66 @@
+function reconstructed=ImageDenoise_reconstruct(y, Problem, SparseDict)
+%%  Image Denoising Problem reconstruction function
+%   
+%   This reconstruction function is using sparse representation y 
+%   in dictionary Problem.A to reconstruct the patches of the denoised
+%   image.
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
+%%
+
+
+% stepsize %
+if (isfield(Problem,'stepsize'))
+  stepsize = Problem.stepsize;
+  if (numel(stepsize)==1)
+    stepsize = ones(1,2)*stepsize;
+  end
+else
+  stepsize = ones(1,2);
+end
+if (any(stepsize<1))
+  error('Invalid step size.');
+end
+
+% lambda %
+if (isfield(Problem,'lambda'))
+  lambda = Problem.lambda;
+else
+  lambda = Problem.maxval/(10*Problem.sigma);
+end
+if exist('SparseDict','var')&&(SparseDict==1)
+    if issparse(Problem.A)
+        A = Problem.A;
+      else
+        A = sparse(Problem.A);
+      end
+    cl_samp=add_dc(dictsep(Problem.basedict,A,y), Problem.b1dc,'columns');
+else
+    cl_samp=add_dc(Problem.A*y, Problem.b1dc,'columns');
+end
+%   combine the patches into reconstructed image
+cl_im=col2imstep(cl_samp, size(Problem.Noisy), Problem.blocksize);
+
+cnt = countcover(size(Problem.Noisy),Problem.blocksize,stepsize);
+
+im = (cl_im+lambda*Problem.Noisy)./(cnt + lambda);
+% y(y~=0)=1;
+% numD=sum(y,2);
+% nnzy=sum(y,1);
+% figure(200);plot(sort(numD));
+% figure(201);plot(sort(nnzy));
+[v.RMSErn, v.RMSEcd, v.rn_im, v.cd_im]=SMALL_vmrse_type2(Problem.Original, Problem.Noisy, im);
+%% output structure image+psnr %%
+reconstructed.Image=im;
+reconstructed.psnr = 20*log10(Problem.maxval * sqrt(numel(Problem.Original(:))) / norm(Problem.Original(:)-im(:)));
+reconstructed.vmrse=v;
+reconstructed.ssim=SMALL_ssim_index(Problem.Original, im);
+end
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/generateAMTProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,91 @@
+function data = generateAMTProblem(nfft, windowSize, overlap)
+%%  Generate Automatic Music Transcription Problem
+%   
+%   generateAMT_Learning_Problem is a part of the SMALLbox and generates
+%   a problem that can be used for comparison of Dictionary Learning/Sparse
+%   Representation techniques in automatic music transcription scenario.
+%   The function prompts a user for an audio file (mid, wav, mat) reads it
+%   and generates a spectrogram given fft size (default nfft=4096), analysis
+%   window size (windowSize=2822), and analysis window overlap (overlap =
+%   0.5).
+%   
+%   The output of the function is stucture with following fields:
+%       b - matrix with magnitudes of the spectrogram
+%       f - vector of frequencies at wihch spectrogram is computed
+%       windowSize - analysis window size
+%       overlap - analysis window overlap
+%       fs - sampling frequency
+%       m - number of frequenciy points in spectrogram
+%       n - number of time points in the spectrogram
+%       p - number of dictionary elements to be learned (eg 88 for piano)
+%       notesOriginal - notes of the original audio to be used for
+%                       comparison (if midi of the original exists)
+%       name - name of the audio file to transcribe
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
+%  
+%%
+FS=filesep;
+if ~ exist( 'nfft', 'var' ) || isempty(nfft), nfft = 4096; end
+if ~ exist( 'windowSize', 'var' ) || isempty(windowSize), windowSize = 2822; end
+if ~ exist( 'overlap', 'var' ) || isempty(overlap), overlap = 0.5; end
+
+%%
+%ask for file name
+TMPpath=pwd;
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'data',FS,'audio']);
+[filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
+[pathstr, name, ext, versn] = fileparts(filename);
+data.name=name;
+
+data.notesOriginal=[];
+
+if strcmp(ext,'.mid')
+    midi=readmidi(filename);
+    data.notesOriginal=midiInfo(midi);
+    y=midi2audio(midi);
+    wavwrite(y, 44100, 16, 'temp.wav');
+    [x.signal, x.fs, x.nbits]=wavread('temp.wav');
+    delete('temp.wav');
+elseif strcmp(ext,'.wav')
+    cd([pathstr1,FS, 'data', FS, 'audio', FS, 'midi']);
+    filename1=[name, '.mid'];
+    if exist(filename1, 'file')
+        midi=readmidi(filename1);
+        data.notesOriginal=midiInfo(midi);
+    end
+    cd([pathstr1,FS, 'data', FS, 'audio', FS, 'wav']);
+    [x.signal, x.fs, x.nbits]=wavread(filename);
+else
+    cd([pathstr1,FS, 'data', FS, 'audio', FS, 'midi']);
+    filename1=[name, '.mid'];
+    if exist(filename1, 'file')
+        midi=readmidi(filename1);
+        data.notesOriginal=midiInfo(midi);
+    end
+    cd([pathstr1,FS, 'data', FS, 'audio', FS, 'mat']);
+    x=load([pathname,filename]);
+end
+%%
+[X, frX]=spectrogram(x.signal, hanning(windowSize), overlap*windowSize, nfft, x.fs);
+%%
+data.b=abs(X);
+data.f=frX;
+data.windowSize=windowSize;
+data.overlap=overlap;
+data.fs=x.fs;
+data.m=size(X,1);
+data.n=size(X,2);
+
+data.p=88; %number of dictionary elements (ie notes to recover)
+cd(TMPpath);
+
+end
--- a/Problems/generateAudioDeclippingProblem.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/Problems/generateAudioDeclippingProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -5,6 +5,35 @@
 %   Audio declipping is a problem proposed in Audio Inpaining Toolbox and
 %   in [2]. 
 %
+%   The function takes as an optional input 
+%       soundfile   - name of the file
+%       clippingLevel - (default 0.6)
+%       windowSize  - 1D frame size (eg 512)
+%       overlap     - ammount of overlaping frames between 0 and 1
+%       wa,ws,wd    - analisys, synthesis and dictionary window functions
+%       
+%       Dict_fun    - function to be used to generate dictionary 
+%       redundancyFactor - overcompletness of dictionary (default 2)
+%   
+%   The function outputs the structure with following fields:
+%       original    - original signal
+%       clipped     - clipped signal
+%       clipMask    - mask indicating clipped samples
+%       clippingLevel - (default 0.6)
+%       Upper_Limit - maximum value of original data
+%       fs          - sample rate of the original signal in Hertz
+%       nbits       - the number of bits per sample
+%       sigma       - added noise level
+%       B           - dictionary to be used for sparse representation
+%       M           - measurement matrix (non-clipped data in b)
+%       b           - matrix of clipped frames
+%       m           - size od dictionary atom 
+%       n           - number of frames to be represented
+%       p           - number of atoms in dictionary
+%       windowSize  - 1D frame size (eg 512)
+%       overlap     - ammount of overlaping frames between 0 and 1
+%       wa,ws, wd   - analisys, synthesis and dictionary window functions
+%
 %   [1] I. Damnjanovic, M. E. P. Davies, and M. P. Plumbley "SMALLbox - an 
 %   evaluation framework for sparse representations and dictionary 
 %   learning algorithms," V. Vigneron et al. (Eds.): LVA/ICA 2010, 
@@ -103,7 +132,7 @@
 
 data.fs = x.fs;
 data.nbits = x.nbits;
-data.Upper_Limit = max(solutiondata.XClean);
+data.Upper_Limit = max(solutionData.xClean);
 [data.m, data.n] = size(x_clip);
 data.p = windowSize*redundancyFactor; %number of dictionary elements 
 
--- a/Problems/generateAudioDenoiseProblem.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/Problems/generateAudioDenoiseProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -1,20 +1,40 @@
-function data=generateAudioDenoiseProblem(au, trainnum, blocksize, dictsize, overlap, sigma, gain, maxval, initdict);
-%%  Audio Denoising Problem - needs revision, not yet finalised
+function data = generateAudioDenoiseProblem(soundfile, sigma, windowSize,...
+    overlap, wa, ws, trainnum, redundancyFactor, initdict)
+%%  Audio Denoising Problem
 %
 %   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
+%   techniques in audio denoising scenario.
+%
+%   The function takes as an optional input 
+%       soundfile   - name of the file
+%       sigma       - noise level (dB)
+%       windowSize  - 1D frame size (eg 512)
+%       overlap     - ammount of overlaping frames between 0 and 1
+%       wa,ws       - analisys and synthesis window functions
+%       
+%       trainnum    - number of frames for training 
+%       redundancyFactor - overcompletness of dictionary (default 2)
+%       initdict    - initial dictionary
 %   
+%   The function outputs the structure with following fields:
+%       Original    - original signal
+%       Noisy       - signal with added noise
+%       fs          - sample rate of the original signal in Hertz
+%       nbits       - the number of bits per sample
+%       sigma       - added noise level
+%       b           - matrix of training samples for dictionary learning
+%       b1          - matrix containing all frames for reconstruction step
+%       m           - size od dictionary atom 
+%       n           - number of frames for training
+%       p           - number of atoms in dictionary
+%       windowSize  - 1D frame size (eg 512)
+%       overlap     - ammount of overlaping frames between 0 and 1
+%       wa,ws       - analisys and synthesis window functions
+%       initdict	- initial dictionary
 
 %   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2010 Ivan Damnjanovic.
+%   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
@@ -30,67 +50,69 @@
 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)
+if ~ exist( 'soundfile', 'var' ) || isempty(soundfile)
+    %ask for file name 
     [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
-    cd([pathstr1,FS,'data',FS,'audio',FS,'wav']);
-    [filename,pathname] = uigetfile({'*.wav;'},'Select a wav file');
+    cd([pathstr1,FS,'data',FS,'audio']);
+    [filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
     [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');%
-
-
+    if strcmp(ext,'.mid')
+        midi=readmidi(filename);
+%         data.notesOriginal=midiInfo(midi);
+        y=midi2audio(midi);
+        wavwrite(y, 44100, 16, 'temp.wav');
+        [x.signal, x.fs, x.nbits]=wavread('temp.wav');
+        delete('temp.wav');
+    elseif strcmp(ext,'.wav')
+%         cd([pathstr1,FS, 'data', FS, 'audio', FS, 'midi']);
+%         filename1=[name, '.mid'];
+%         if exist(filename1, 'file')
+%             midi=readmidi(filename1);
+%             data.notesOriginal=midiInfo(midi);
+%         end
+        cd([pathstr1,FS, 'data', FS, 'audio', FS, 'wav']);
+        [x.signal, x.fs, x.nbits]=wavread(filename);
+    else
+%         cd([pathstr1,FS, 'data', FS, 'audio', FS, 'midi']);
+%         filename1=[name, '.mid'];
+%         if exist(filename1, 'file')
+%             midi=readmidi(filename1);
+%             data.notesOriginal=midiInfo(midi);
+%         end
+        cd([pathstr1,FS, 'data', FS, 'audio', FS, 'mat']);
+        x=load([pathname,filename]);
+    end
+else
+    [x.signal, x.fs, x.nbits]=wavread(soundfile);
+    [pathstr, name, ext, versn] = fileparts(soundfile);
+    data.name=name;
+end
 
 %% set parameters %%
+if ~ exist( 'sigma', 'var' ) || isempty(sigma), sigma = 0.2; end
 
-x = aunoise;
-if ~ exist( 'blocksize', 'var' ) || isempty(blocksize),blocksize = 512;end
-if ~ exist( 'dictsize', 'var' ) || isempty(dictsize), dictsize = 2048;end
+if ~ exist( 'windowSize', 'var' ) || isempty(windowSize), windowSize = 256;end
+if ~ exist( 'overlap', 'var' ) || isempty(overlap), overlap = 0.5; end
+if ~ exist( 'wa', 'var' ) || isempty(wa), wa = @wSine; end % Analysis window
+if ~ exist( 'ws', 'var' ) || isempty(ws), ws = @wSine; end % Synthesis window
 
-if ~ exist( 'trainnum', 'var' ) || isempty(trainnum),trainnum = (size(x,1)-blocksize+1);end
 
+if ~ exist( 'redundancyFactor', 'var' ) || isempty(windowSize),...
+        redundancyFactor = 2;end
+if ~ exist( 'initdict', 'var' ) || isempty(initdict),...
+        initdict = 'odct'; end
+if ~ exist( 'trainnum', 'var' ) || isempty(trainnum), ...
+        trainnum = 16*redundancyFactor*windowSize;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);
+    initdict = odctndict(windowSize, redundancyFactor*windowSize, 1);
 elseif (strcmpi(initdict,'data'))
     clear initdict;    % causes initialization using random examples
 else
@@ -98,45 +120,31 @@
 end
 
 if exist( 'initdict', 'var' ) 
-  initdict = initdict(:,1:dictsize);
+  initdict = initdict(:,1:redundancyFactor*windowSize);
 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 %%%
 
+%% generate noisy audio %%
 
-X = buffer( x(1:trainnum),blocksize, overlap*blocksize);
+disp(' ');
+disp('Generating noisy audio...');
+x.signal = x.signal/max(abs(x.signal(:)))*0.99999;
+n = randn(size(x.signal)) .* sigma;
+
+xnoise = x.signal + n;%  here we can load noise audio if available 
+                 %  for example: wavread('icassp06_x.wav');%
+
+                 
+
+
+X = im2colstep(xnoise,[windowSize 1],[overlap*windowSize 1]);
+X = diag(wa(windowSize)) * X;
+
+
+
+
 
 % remove dc in blocks to conserve memory %
 % bsize = 2000;
@@ -144,17 +152,32 @@
 %   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.Original = x.signal;
+data.Noisy = xnoise;
+data.fs = x.fs;
+data.nbits = x.nbits;
+
 data.sigma = sigma;
-data.gain = gain;
-data.maxval = maxval;
+
+
+if (trainnum<size(X,2))
+    p = randperm(size(X,2));
+    p=sort(p(1:trainnum));
+    data.b = X(:,p);
+else
+    data.b = X;
+end
+
+data.b1 = X;
+[data.m, data.n] = size(data.b);
+data.p = redundancyFactor*windowSize;
+
+data.windowSize = windowSize;
+data.overlap = overlap;
+data.ws = ws;
+data.wa = wa;
+
 data.initdict= initdict;
-data.signalDim=1;
+
 cd(TMPpath);
 
--- a/Problems/generateImageDenoiseProblem.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/Problems/generateImageDenoiseProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -1,4 +1,5 @@
-function data=generateImageDenoiseProblem(im, trainnum, blocksize, dictsize, sigma, gain, maxval, initdict);
+function data = generateImageDenoiseProblem(im, trainnum, blocksize,...
+    dictsize, sigma, gain, maxval, initdict)
 %%  Generate Image Denoising Problem
 %   
 %   generateImageDenoiseProblem is a part of the SMALLbox and generates
--- a/Problems/generateMyDummyProblem.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/Problems/generateMyDummyProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -13,7 +13,7 @@
 
 %%  Change copyright notice as appropriate:
 %   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2009 Ivan Damnjanovic.
+%   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
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Problems/generatePierreProblem.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,121 @@
+function data=generatePierreProblem(src, trg, blocksize, dictsize);
+%%  Generate Pierre Villars Problem
+%
+%   Pierre_Problem is a part of the SMALLbox and generates the problem
+%   suggested by Professor Pierre Vandergheynst on the SMALL meeting in 
+%   Villars.
+%   The function takes as an input:
+%   -   src - source image matrix (if not present function promts user for 
+%             an image file) ,
+%   -   trg - target image matrix (if not present function promts user for 
+%             an image file) ,
+%   -   blocksize - block (patch) vertical/horizontal dimension (default 8),
+%   -   dictsize - dictionary size (default - all patches from target
+%   image).
+%
+%   The output of the function is stucture with following fields:
+%   -   srcname - source image name,
+%   -   imageSrc - source image matrix,
+%   -   trgname - target image name,
+%   -   imageTrg - Target image matrix,
+%   -   A - dictonary with patches from the source image,
+%   -   b - measurement matrix (i.e. patches from target image to be
+%           represented in dictionary A,
+%   -   m - size of patches (default 25),
+%   -   n - number of patches to be represented,
+%   -   p - dictionary size,
+%   -   blocksize - block size (default [5 5]),
+%   -   maxval - maximum value (default - 255)
+%   -   sparse - if 1 SMALL_solve will keep solution matrix in sparse form,
+%                due to memory constrains.
+
+%
+%   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.
+%% prompt user for images %%
+
+%   ask for source file name
+
+TMPpath=pwd;
+FS=filesep;
+if ~ exist( 'src', 'var' ) || isempty(src)
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'data',FS,'images']);
+[filename,pathname] = uigetfile({'*.png;'},'Select a source image');
+[pathstr, name, ext, versn] = fileparts(filename);
+data.srcname=name;
+src = imread(filename);
+src = double(src);
+end;
+
+%   ask for target file name
+
+if ~ exist( 'trg', 'var' ) || isempty(trg)
+[filename,pathname] = uigetfile({'*.png;'},'Select a target image');
+[pathstr, name, ext, versn] = fileparts(filename);
+data.trgname=name;
+trg = imread(filename);
+trg = double(trg);
+end;
+cd(TMPpath);
+
+%% set parameters %%
+
+maxval = 255;
+if ~ exist( 'blocksize', 'var' ) || isempty(blocksize),blocksize = 5;end
+
+if ~ exist( 'dictsize', 'var' ) || isempty(dictsize),
+    dictsize = (size(src,1)-blocksize+1)*(size(src,2)-blocksize+1);
+    patch_idx=1:dictsize;
+else  
+    num_blocks_src=(size(src,1)-blocksize+1)*(size(src,2)-blocksize+1);
+    patch_idx=1:floor(num_blocks_src/dictsize):dictsize*floor(num_blocks_src/dictsize);
+end
+
+p = ndims(src);
+if (p==2 && any(size(src)==1) && length(blocksize)==1)
+  p = 1;
+end
+
+
+% blocksize %
+if (numel(blocksize)==1)
+  blocksize = ones(1,p)*blocksize;
+end
+%%
+%% create dictionary data %%
+
+S=im2colstep(src,blocksize);
+
+for j= 1:size(S,2)
+    S(:,j)=S(:,j)./norm(S(:,j));
+end
+
+%% create measurement matrix %%
+
+T=im2colstep(trg,blocksize, blocksize);
+
+%% output structure %%
+
+data.imageSrc = src;
+data.imageTrg = trg;
+data.A = S(:,patch_idx);
+data.b = T;
+data.m = size(T,1);
+data.n = size(T,2);
+data.p = size(data.A,2);
+data.blocksize=blocksize;
+data.maxval=maxval;
+
+%   keep coefficients matrix in sparse form and do not convert it to full.
+%   getting around out of memory problem when converting big matrix from
+%   sparse to full... (check SMALL_solve function)
+data.sparse=1;
+
+
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/ALPS solvers tests/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepALPSandMahile.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,288 @@
+%%  Dictionary Learning for Image Denoising - KSVD vs Recursive Least Squares
+%
+%   This file contains an example of how SMALLbox can be used to test different
+%   dictionary learning techniques in Image Denoising problem.
+%   It calls generateImageDenoiseProblem that will let you to choose image,
+%   add noise and use noisy image to generate training set for dictionary
+%   learning.
+%   Two dictionary learning techniques were compared:
+%   -   KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient
+%              Implementation of the K-SVD Algorithm using Batch Orthogonal
+%              Matching Pursuit", Technical Report - CS, Technion, April 2008.
+%   -   RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares
+%       Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on,
+%       vol.58, no.4, pp.2121-2130, April 2010
+%
+
+
+%   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.
+%   
+%%
+
+
+
+%   If you want to load the image outside of generateImageDenoiseProblem
+%   function uncomment following lines. This can be useful if you want to
+%   denoise more then one image for example.
+%   Here we are loading test_image.mat that contains structure with 5 images : lena,
+%   barbara,boat, house and peppers.
+clear;
+TMPpath=pwd;
+FS=filesep;
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'data',FS,'images']);
+load('test_image.mat');
+cd(TMPpath);
+
+%   Deffining the noise levels that we want to test
+
+noise_level=[10 20 25 50 100];
+
+%   Here we loop through different noise levels and images 
+
+for noise_ind=4:4
+for im_num=1:1
+
+% Defining Image Denoising Problem as Dictionary Learning
+% Problem. As an input we set the number of training patches.
+
+SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',256, noise_level(noise_ind));
+SMALL.Problem.name=int2str(im_num);
+
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+
+%   results structure is to store all results
+
+results(noise_ind,im_num).noisy_psnr=SMALL.Problem.noisy_psnr;
+
+%%
+%   Use KSVD Dictionary Learning Algorithm to Learn overcomplete dictionary
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(1)=SMALL_init_DL();
+
+% Defining the parameters needed for dictionary learning
+
+SMALL.DL(1).toolbox = 'KSVD';
+SMALL.DL(1).name = 'ksvd';
+
+%   Defining the parameters for KSVD
+%   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(...
+    'Edata', Edata,...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'exact', 1, ...
+    'iternum', 20,...
+    'memusage', 'high');
+
+%   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) ImageDenoise_reconstruct(x, SMALL.Problem);
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(1)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(1).toolbox='ompbox';
+SMALL.solver(1).name='omp2';
+SMALL.solver(1).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+
+%   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));
+
+%   Show PSNR after reconstruction
+
+SMALL.solver(1).reconstructed.psnr
+
+%%
+%   For comparison purposes we will denoise image with overcomplete DCT
+%   here
+%   Set SMALL.Problem.A dictionary to be oDCT (i.e. Problem.initdict -
+%   since initial dictionaruy is already set to be oDCT when generating the
+%   denoising problem
+
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(2)=SMALL_init_solver('ALPS','AgebraicPursuit','',1);
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(2).param=struct(...
+    'tolerance',1e-05,...
+    'sparsity', 32,...
+    'mode', 0,...
+    'memory', 1,...
+    'iternum', 50); 
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(2)=SMALL_init_DL('TwoStepDL', 'Mailhe', '', 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(2).param=struct(...
+    'solver', SMALL.solver(2),...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'iternum', 40,...
+    'show_dict', 1);
+
+%   Learn the dictionary
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   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(2).D;
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+%%
+% In the b1 field all patches from the image are stored. For RLS-DLA we
+% will first exclude all the patches that have l2 norm smaller then
+% threshold and then take min(40000, number_of_remaining_patches) in
+% ascending order as our training set (SMALL.Problem.b)
+
+X=SMALL.Problem.b1;
+X_norm=sqrt(sum(X.^2, 1));
+[X_norm_sort, p]=sort(X_norm);
+p1=p(X_norm_sort>Edata);
+if size(p1,2)>40000
+    p2 = randperm(size(p1,2));
+    p2=sort(p2(1:40000));
+    size(p2,2)
+    SMALL.Problem.b=X(:,p1(p2));
+else 
+    size(p1,2)
+    SMALL.Problem.b=X(:,p1);
+
+end
+
+%   Forgetting factor for RLS-DLA algorithm, in this case we are using
+%   fixed value
+
+lambda=0.9998
+
+%   Use Recursive Least Squares
+%   to Learn overcomplete dictionary 
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(3)=SMALL_init_DL();
+
+%   Defining fields needed for dictionary learning
+
+SMALL.DL(3).toolbox = 'SMALL';
+SMALL.DL(3).name = 'SMALL_rlsdla';
+SMALL.DL(3).param=struct(...
+    'Edata', Edata,...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'forgettingMode', 'FIX',...
+    'forgettingFactor', lambda,...
+    'show_dict', 1000);
+
+
+SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.Problem.A = SMALL.DL(3).D;
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
+
+SMALL.solver(3)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(3).toolbox='ompbox';
+SMALL.solver(3).name='omp2';
+SMALL.solver(3).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+
+
+SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+
+SMALL.solver(3).reconstructed.psnr
+
+
+% show results %
+
+SMALL_ImgDeNoiseResult(SMALL);
+
+results(noise_ind,im_num).psnr.ksvd=SMALL.solver(1).reconstructed.psnr;
+results(noise_ind,im_num).psnr.odct=SMALL.solver(2).reconstructed.psnr;
+results(noise_ind,im_num).psnr.rlsdla=SMALL.solver(3).reconstructed.psnr;
+results(noise_ind,im_num).vmrse.ksvd=SMALL.solver(1).reconstructed.vmrse;
+results(noise_ind,im_num).vmrse.odct=SMALL.solver(2).reconstructed.vmrse;
+results(noise_ind,im_num).vmrse.rlsdla=SMALL.solver(3).reconstructed.vmrse;
+results(noise_ind,im_num).ssim.ksvd=SMALL.solver(1).reconstructed.ssim;
+results(noise_ind,im_num).ssim.odct=SMALL.solver(2).reconstructed.ssim;
+results(noise_ind,im_num).ssim.rlsdla=SMALL.solver(3).reconstructed.ssim;
+
+results(noise_ind,im_num).time.ksvd=SMALL.solver(1).time+SMALL.DL(1).time;
+results(noise_ind,im_num).time.rlsdla.time=SMALL.solver(3).time+SMALL.DL(3).time;
+clear SMALL;
+end
+end
+% save results.mat results
--- a/examples/ALPS solvers tests/SMALL_solver_test_ALPS.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/ALPS solvers tests/SMALL_solver_test_ALPS.m	Thu Nov 17 11:22:17 2011 +0000
@@ -88,7 +88,7 @@
 % SMALL Conjugate Gradient test 
 SMALL.solver(i)=SMALL_init_solver;
 SMALL.solver(i).toolbox='SMALL';    
-SMALL.solver(i).name='SMALL_cgp';
+SMALL.solver(i).name='SMALL_pcgp';
 
 % In the following string all parameters except matrix, measurement vector
 % and size of solution need to be specified. If you are not sure which
--- a/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Thu Nov 17 11:22:17 2011 +0000
@@ -31,7 +31,7 @@
 %   Defining Automatic Transcription of Piano tune as Dictionary Learning
 %   Problem
 
-SMALL.Problem = generateAMT_Learning_Problem();
+SMALL.Problem = generateAMTProblem();
 
 %%
 %   Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in
@@ -67,7 +67,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) SMALL_midiGenerate(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -151,7 +151,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct=@(x) SMALL_midiGenerate(x, SMALL.Problem);
+SMALL.Problem.reconstruct=@(x) AMT_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
--- a/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m	Thu Nov 17 11:22:17 2011 +0000
@@ -33,7 +33,7 @@
 % Defining Automatic Transcription of Piano tune as Dictionary Learning
 % Problem
 
-SMALL.Problem = generateAMT_Learning_Problem();
+SMALL.Problem = generateAMTProblem();
 TPmax=0;
 for i=1:5
     %%
@@ -76,7 +76,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(i).D;
-    SMALL.Problem.reconstruct = @(x) SMALL_midiGenerate(x, SMALL.Problem);
+    SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
--- a/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m	Thu Nov 17 11:22:17 2011 +0000
@@ -33,7 +33,7 @@
 % Defining Automatic Transcription of Piano tune as Dictionary Learning
 % Problem
 
-SMALL.Problem = generateAMT_Learning_Problem();
+SMALL.Problem = generateAMTProblem();
 
 TPmax=0;
 
@@ -73,7 +73,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(i).D;
-    SMALL.Problem.reconstruct = @(x) SMALL_midiGenerate(x, SMALL.Problem);
+    SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
--- a/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Thu Nov 17 11:22:17 2011 +0000
@@ -33,7 +33,7 @@
 % Defining Automatic Transcription of Piano tune as Dictionary Learning
 % Problem
 
-SMALL.Problem = generateAMT_Learning_Problem();
+SMALL.Problem = generateAMTProblem();
 TPmax=0;
 %%
 for i=1:10
@@ -77,7 +77,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(i).D;
-    SMALL.Problem.reconstruct=@(x) SMALL_midiGenerate(x, SMALL.Problem);
+    SMALL.Problem.reconstruct=@(x) AMT_reconstruct(x, SMALL.Problem);
     
     
     %%
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Thu Nov 17 11:22:17 2011 +0000
@@ -99,7 +99,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -140,7 +140,7 @@
 %   Setting up reconstruction function
 
 SparseDict=0;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem, SparseDict);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem, SparseDict);
 
 %   Initialising solver structure
 %   Setting solver structure fields (toolbox, name, param, solution,
@@ -217,7 +217,7 @@
 %   reconstructed and time) to zero values
 
 SMALL.Problem.A = SMALL.DL(3).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 SMALL.solver(3)=SMALL_init_solver;
 
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLAvsTwoStepMOD.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLAvsTwoStepMOD.m	Thu Nov 17 11:22:17 2011 +0000
@@ -9,10 +9,7 @@
 %   -   KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient
 %              Implementation of the K-SVD Algorithm using Batch Orthogonal
 %              Matching Pursuit", Technical Report - CS, Technion, April 2008.
-%   -   RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares
-%       Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on,
-%       vol.58, no.4, pp.2121-2130, April 2010
-%
+
 
 
 %   Centre for Digital Music, Queen Mary, University of London.
@@ -102,7 +99,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -183,7 +180,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
@@ -247,7 +244,7 @@
 %   reconstructed and time) to zero values
 
 SMALL.Problem.A = SMALL.DL(3).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 SMALL.solver(3)=SMALL_init_solver;
 
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Thu Nov 17 11:22:17 2011 +0000
@@ -97,7 +97,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -175,7 +175,7 @@
 %   Setting up reconstruction function
 
 SparseDict=1;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem, SparseDict);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem, SparseDict);
 
 %   Initialising solver structure
 %   Setting solver structure fields (toolbox, name, param, solution,
@@ -237,7 +237,7 @@
 
 %   Setting up reconstruction function
 
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %   Initialising solver structure
 %   Setting solver structure fields (toolbox, name, param, solution,
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepKSVD.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepKSVD.m	Thu Nov 17 11:22:17 2011 +0000
@@ -103,7 +103,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -182,7 +182,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Thu Nov 17 11:22:17 2011 +0000
@@ -90,7 +90,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Thu Nov 17 11:22:17 2011 +0000
@@ -100,7 +100,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(1).D;
-    SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+    SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
@@ -167,7 +167,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(2).D;
-    SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+    SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_TwoStep_KSVD_MOD_OLS_Mailhe.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_TwoStep_KSVD_MOD_OLS_Mailhe.m	Thu Nov 17 11:22:17 2011 +0000
@@ -111,7 +111,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
@@ -170,7 +170,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
@@ -230,7 +230,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(3).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
@@ -290,7 +290,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(4).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
--- a/examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m	Thu Nov 17 11:22:17 2011 +0000
@@ -38,7 +38,7 @@
 %   Defining Automatic Transcription of Piano tune as Dictionary Learning
 %   Problem
 
-SMALL.Problem = generateAMT_Learning_Problem('',2048,0.75);
+SMALL.Problem = generateAMTProblem('',2048,0.75);
 
 %%
 %   Use KSVD Dictionary Learning Algorithm to Learn 88 notes (defined in
@@ -74,7 +74,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) SMALL_midiGenerate(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) AMT_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -89,7 +89,7 @@
 %   Defining the parameters needed for sparse representation
 
 SMALL.solver(1).toolbox='SMALL';
-SMALL.solver(1).name='SMALL_cgp';
+SMALL.solver(1).name='SMALL_pcgp';
 
 %   Here we use mexLasso mode=2, with lambda=2, lambda2=0 and positivity
 %   constrain (type 'help mexLasso' for more information about modes):
@@ -216,7 +216,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct=@(x) SMALL_midiGenerate(x, SMALL.Problem);
+SMALL.Problem.reconstruct=@(x) AMT_reconstruct(x, SMALL.Problem);
 
 
 %   Call SMALL_soolve to represent the signal in the given dictionary. 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/MajorizationMinimization tests/SMALL_AudioDenoise_DL_test_KSVDvsSPAMS.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,128 @@
+%% 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);
+    
\ No newline at end of file
--- a/examples/MajorizationMinimization tests/SMALL_ImgDenoise_DL_test_KSVDvsMajorizationMinimization.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/MajorizationMinimization tests/SMALL_ImgDenoise_DL_test_KSVDvsMajorizationMinimization.m	Thu Nov 17 11:22:17 2011 +0000
@@ -103,7 +103,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -184,7 +184,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(2).D;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+SMALL.Problem.reconstruct = @(x) ImageDenoise_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
--- a/examples/Pierre Villars/Pierre_Villars_Example.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/Pierre Villars/Pierre_Villars_Example.m	Thu Nov 17 11:22:17 2011 +0000
@@ -23,7 +23,7 @@
 
 %   Defining the Problem structure
 
-SMALL.Problem = generatePierre_Problem();
+SMALL.Problem = generatePierreProblem();
 
 %   Show original image and image that is used as a dictionary
 figure('Name', 'Original and Dictionary Image');
--- a/examples/SMALL_solver_test.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/SMALL_solver_test.m	Thu Nov 17 11:22:17 2011 +0000
@@ -81,7 +81,7 @@
 % SMALL Conjugate Gradient test 
 SMALL.solver(i)=SMALL_init_solver;
 SMALL.solver(i).toolbox='SMALL';    
-SMALL.solver(i).name='SMALL_cgp';
+SMALL.solver(i).name='SMALL_pcgp';
 
 % In the following string all parameters except matrix, measurement vector
 % and size of solution need to be specified. If you are not sure which
--- a/examples/SMALL_solver_test_Audio.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/examples/SMALL_solver_test_Audio.m	Thu Nov 17 11:22:17 2011 +0000
@@ -61,7 +61,7 @@
 
 SMALL.solver(i)=SMALL_init_solver;
 SMALL.solver(i).toolbox='SMALL';    
-SMALL.solver(i).name='SMALL_cgp';
+SMALL.solver(i).name='SMALL_pcgp';
 
 % In the following string all parameters except matrix, measurement vector
 % and size of solution need to be specified. If you are not sure which
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/solvers/SMALL_pcgp.m	Thu Nov 17 11:22:17 2011 +0000
@@ -0,0 +1,113 @@
+function [A, resF]=SMALL_pcgp(Dict,X, m,  maxNumCoef, errorGoal, varargin) 
+%%  Partial Conjugate Gradient Pursuit Multiple vectors sparse representation
+%   
+%   Sparse coding of a group of signals based on a given 
+%   dictionary and specified number of atoms to use. 
+%   input arguments: Dict - the dictionary
+%                  X - the signals to represent
+%                  m - number of atoms in Dictionary
+%                  errorGoal - the maximal allowed representation error for
+%                  each signal.
+%
+%   optional:        if Dict is function handle then Transpose Dictionary
+%                  handle needs to be specified.
+%
+%   output arguments: A - sparse coefficient matrix.
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
+%
+%%
+
+% This Dictionary check is based on Thomas Blumensath work in sparsify 0_4 greedy solvers 
+if     isa(Dict,'float')      
+            D =@(z) Dict*z;  
+            Dt =@(z) Dict'*z;
+elseif isobject(Dict) 
+            D =@(z) Dict*z;  
+            Dt =@(z) Dict'*z;
+elseif isa(Dict,'function_handle') 
+    try
+        DictT=varargin{1};
+        if isa(DictT,'function_handle'); 
+                D=Dict; 
+                Dt=DictT;
+        else
+                error('If Dictionary is a function handle,Transpose Dictionary also needs to be a function handle. ');
+        end
+    catch
+        error('If Dictionary is a function handle, Transpose Dictionary  needs to be specified. Exiting.');
+    end
+else
+    error('Dictionary is of unsupported type. Use explicit matrix, function_handle or object. Exiting.');
+end
+%%
+%positivity=1;
+[n,P]=size(X);
+
+
+
+A = sparse(m,size(X,2));
+
+for k=1:1:P,
+    
+    x = X(:,k);
+    residual = x;
+	indx =[];
+	j=0;
+   
+	
+    currResNorm = residual'*residual/n;
+    errGoal=errorGoal*currResNorm;
+    a = zeros(m,1);
+    p = zeros(m,1);
+    
+    while  j < maxNumCoef,
+        
+        j = j+1;
+        dir=Dt(residual);
+        if exist('positivity','var')&&(positivity==1)
+            [tmp__, pos]=max(dir);
+        else
+            [tmp__, pos]=max(abs(dir));
+        end
+        indx(j)=pos;
+        
+        p(indx)=dir(indx);
+        Dp=D(p);
+        pDDp=Dp'*Dp;
+        
+        if (j==1) 
+            beta=0;
+        else
+            beta=-Dp'*residual/pDDp;
+        end
+        
+        alfa=residual'*Dp/pDDp;
+        a=a+alfa*p;
+        p(indx)=dir(indx)+beta*p(indx);
+        
+        residual=residual-alfa*Dp;
+    
+		currResNorm = residual'*residual/n;
+        if currResNorm<errGoal
+            fprintf('\nFound exact representation! \n');
+            break;
+        end
+   end;
+   if (~isempty(indx))
+       resF(k)=currResNorm;
+       A(indx,k)=a(indx);
+   end
+end;
+return;
+
+
+
--- a/util/SMALL_AudioDeNoiseResult.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/util/SMALL_AudioDeNoiseResult.m	Thu Nov 17 11:22:17 2011 +0000
@@ -2,7 +2,7 @@
 %% Plots the results of Audio denoising experiment - underconstruction
 
 %   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2009 Ivan Damnjanovic.
+%   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
@@ -13,7 +13,7 @@
 
 fMain=figure('Name', sprintf('File %s (training set size- %d, sigma - %d)',SMALL.Problem.name, SMALL.Problem.n, SMALL.Problem.sigma));
 m=size(SMALL.solver,2);
-maxval=SMALL.Problem.maxval;
+maxval=max(SMALL.Problem.Original);
 au=SMALL.Problem.Original;
 aunoise=SMALL.Problem.Noisy;
 
@@ -25,7 +25,7 @@
 
 for i=1:m
     params=SMALL.solver(i).param;
-    sWav=subplot(2, m, m+i, 'Parent', fMain); plot(SMALL.solver(i).reconstructed.Image/maxval, 'Parent', sWav);
+    sWav=subplot(2, m, m+i, 'Parent', fMain); plot(SMALL.solver(i).reconstructed.audio/maxval, 'Parent', sWav);
     title(sprintf('%s Denoised audio, PSNR: %.2fdB', SMALL.DL(i).name, SMALL.solver(i).reconstructed.psnr),'Parent', sWav );
     if strcmpi(SMALL.DL(i).name,'ksvds')
         D = kron(SMALL.Problem.basedict{2},SMALL.Problem.basedict{1})*SMALL.DL(i).D;
--- a/util/SMALL_solve.m	Thu Nov 17 11:18:25 2011 +0000
+++ b/util/SMALL_solve.m	Thu Nov 17 11:22:17 2011 +0000
@@ -1,5 +1,5 @@
 function solver = SMALL_solve(Problem, solver)
-%% SMALL sparse solver
+%% SMALL sparse solver caller function
 %
 %   Function gets as input SMALL structure that contains SPARCO problem to
 %   be solved, name of the toolbox and solver, and parameters file for
@@ -88,7 +88,7 @@
     [y, numiter, time, y_path] = wrapper_ALPS_toolbox(b, A, solver.param);
 elseif (strcmpi(solver.toolbox, 'MMbox'))
     if ~isa(Problem.A,'float')
-        % ALPS does not accept implicit dictionary definition
+        % MMbox does not accept implicit dictionary definition
         A = opToMatrix(Problem.A, 1);
     end