idamnjanovic@8: function SMALL_AudioDeNoiseResult(SMALL)
idamnjanovic@8: 
idamnjanovic@8: fMain=figure('Name', sprintf('File %s (training set size- %d, sigma - %d)',SMALL.Problem.name, SMALL.Problem.n, SMALL.Problem.sigma));
idamnjanovic@8: m=size(SMALL.solver,2);
idamnjanovic@8: maxval=SMALL.Problem.maxval;
idamnjanovic@8: au=SMALL.Problem.Original;
idamnjanovic@8: aunoise=SMALL.Problem.Noisy;
idamnjanovic@8: 
idamnjanovic@8: subplot(2, m, 1); plot(au/maxval);
idamnjanovic@8: title('Original audio');
idamnjanovic@8: 
idamnjanovic@8: subplot(2,m,2); plot(aunoise/maxval);
idamnjanovic@8: title(sprintf('Noisy audio, PSNR = %.2fdB', 20*log10(maxval * sqrt(numel(au)) / norm(au(:)-aunoise(:))) ));
idamnjanovic@8: 
idamnjanovic@8: for i=1:m
idamnjanovic@8:     params=SMALL.solver(i).param;
idamnjanovic@8:     sWav=subplot(2, m, m+i, 'Parent', fMain); plot(SMALL.solver(i).reconstructed.Image/maxval, 'Parent', sWav);
idamnjanovic@8:     title(sprintf('%s Denoised audio, PSNR: %.2fdB', SMALL.DL(i).name, SMALL.solver(i).reconstructed.psnr),'Parent', sWav );
idamnjanovic@8:     if strcmpi(SMALL.DL(i).name,'ksvds')
idamnjanovic@8:         D = kron(SMALL.Problem.basedict{2},SMALL.Problem.basedict{1})*SMALL.DL(i).D;
idamnjanovic@8:     else
idamnjanovic@8:         D = SMALL.DL(i).D;
idamnjanovic@8:     end
idamnjanovic@8:     figure('Name', sprintf('%s dictionary in %.2f s', SMALL.DL(i).name, SMALL.DL(i).time));
idamnjanovic@8:     imshow(D*255);
idamnjanovic@8: %     n= size(D,2);
idamnjanovic@8: %     sqrtn=round(sqrt(size(D,2)));
idamnjanovic@8: %     for j=1:n
idamnjanovic@8: %     subplot(sqrtn,sqrtn,j); plot(D(:,j));
idamnjanovic@8: %     end
idamnjanovic@8: %     dictimg = showdict(D,[params.blocksize 1],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
idamnjanovic@8: %     
idamnjanovic@8: %     subplot(2,m,m+i);imshow(imresize(dictimg,2,'nearest'));
idamnjanovic@8: %     title(sprintf('%s dictionary in %.2f s', SMALL.DL(i-1).name, SMALL.DL(i-1).time));
idamnjanovic@8:     
idamnjanovic@8: end