{"nbformat_minor": 0, "worksheets": [{"cells": [{"source": ["Sound Processing with Short Time Fourier Transform\n", "==================================================\n", "\n*Important:* Please read the [installation page](http://gpeyre.github.io/numerical-tours/installation_matlab/) for details about how to install the toolboxes.\n", "$\\newcommand{\\dotp}[2]{\\langle #1, #2 \\rangle}$\n", "$\\newcommand{\\enscond}[2]{\\lbrace #1, #2 \\rbrace}$\n", "$\\newcommand{\\pd}[2]{ \\frac{ \\partial #1}{\\partial #2} }$\n", "$\\newcommand{\\umin}[1]{\\underset{#1}{\\min}\\;}$\n", "$\\newcommand{\\umax}[1]{\\underset{#1}{\\max}\\;}$\n", "$\\newcommand{\\umin}[1]{\\underset{#1}{\\min}\\;}$\n", "$\\newcommand{\\uargmin}[1]{\\underset{#1}{argmin}\\;}$\n", "$\\newcommand{\\norm}[1]{\\|#1\\|}$\n", "$\\newcommand{\\abs}[1]{\\left|#1\\right|}$\n", "$\\newcommand{\\choice}[1]{ \\left\\{ \\begin{array}{l} #1 \\end{array} \\right. }$\n", "$\\newcommand{\\pa}[1]{\\left(#1\\right)}$\n", "$\\newcommand{\\diag}[1]{{diag}\\left( #1 \\right)}$\n", "$\\newcommand{\\qandq}{\\quad\\text{and}\\quad}$\n", "$\\newcommand{\\qwhereq}{\\quad\\text{where}\\quad}$\n", "$\\newcommand{\\qifq}{ \\quad \\text{if} \\quad }$\n", "$\\newcommand{\\qarrq}{ \\quad \\Longrightarrow \\quad }$\n", "$\\newcommand{\\ZZ}{\\mathbb{Z}}$\n", "$\\newcommand{\\CC}{\\mathbb{C}}$\n", "$\\newcommand{\\RR}{\\mathbb{R}}$\n", "$\\newcommand{\\EE}{\\mathbb{E}}$\n", "$\\newcommand{\\Zz}{\\mathcal{Z}}$\n", "$\\newcommand{\\Ww}{\\mathcal{W}}$\n", "$\\newcommand{\\Vv}{\\mathcal{V}}$\n", "$\\newcommand{\\Nn}{\\mathcal{N}}$\n", "$\\newcommand{\\NN}{\\mathcal{N}}$\n", "$\\newcommand{\\Hh}{\\mathcal{H}}$\n", "$\\newcommand{\\Bb}{\\mathcal{B}}$\n", "$\\newcommand{\\Ee}{\\mathcal{E}}$\n", "$\\newcommand{\\Cc}{\\mathcal{C}}$\n", "$\\newcommand{\\Gg}{\\mathcal{G}}$\n", "$\\newcommand{\\Ss}{\\mathcal{S}}$\n", "$\\newcommand{\\Pp}{\\mathcal{P}}$\n", "$\\newcommand{\\Ff}{\\mathcal{F}}$\n", "$\\newcommand{\\Xx}{\\mathcal{X}}$\n", "$\\newcommand{\\Mm}{\\mathcal{M}}$\n", "$\\newcommand{\\Ii}{\\mathcal{I}}$\n", "$\\newcommand{\\Dd}{\\mathcal{D}}$\n", "$\\newcommand{\\Ll}{\\mathcal{L}}$\n", "$\\newcommand{\\Tt}{\\mathcal{T}}$\n", "$\\newcommand{\\si}{\\sigma}$\n", "$\\newcommand{\\al}{\\alpha}$\n", "$\\newcommand{\\la}{\\lambda}$\n", "$\\newcommand{\\ga}{\\gamma}$\n", "$\\newcommand{\\Ga}{\\Gamma}$\n", "$\\newcommand{\\La}{\\Lambda}$\n", "$\\newcommand{\\si}{\\sigma}$\n", "$\\newcommand{\\Si}{\\Sigma}$\n", "$\\newcommand{\\be}{\\beta}$\n", "$\\newcommand{\\de}{\\delta}$\n", "$\\newcommand{\\De}{\\Delta}$\n", "$\\newcommand{\\phi}{\\varphi}$\n", "$\\newcommand{\\th}{\\theta}$\n", "$\\newcommand{\\om}{\\omega}$\n", "$\\newcommand{\\Om}{\\Omega}$\n"], "metadata": {}, "cell_type": "markdown"}, {"source": ["This numerical tour explores local Fourier analysis of sounds, and its\n", "application to source denoising."], "metadata": {}, "cell_type": "markdown"}, {"source": ["Local Fourier analysis of sound.\n", "--------------------------------\n", "A sound is a 1D signal that is locally highly oscillating and stationary.\n", "A local Fourier analysis is thus usefull to study the property of the\n", "sound such as its local amplitude and frequency.\n", "\n", "\n", "First we load a sound, with a slight sub-sampling"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 5, "cell_type": "code", "language": "python", "metadata": {}, "input": ["n = 1024*16;\n", "options.n = n;\n", "[x,fs] = load_sound('bird', n);"]}, {"source": ["You can actually play a sound.\n", "In case this does not work, you need to run the command\n", "|wavwrite(x(:)', 'tmp.wav')| and click on the saved file |'tmp.wav'|\n", "to read it."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 5, "cell_type": "code", "language": "python", "metadata": {}, "input": ["sound(x(:)',fs);"]}, {"source": ["We can display the sound."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 6, "cell_type": "code", "language": "python", "metadata": {}, "input": ["clf;\n", "plot(1:n,x);\n", "axis('tight');\n", "set_graphic_sizes([], 20);\n", "title('Signal');"]}, {"source": ["Local zooms on the sound show that it is highly oscilating."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 7, "cell_type": "code", "language": "python", "metadata": {}, "input": ["p = 512;\n", "t = 1:n;\n", "clf;\n", "sel = n/4 + (0:p-1);\n", "subplot(2,1,1);\n", "plot(t(sel),x(sel)); axis tight;\n", "sel = n/2 + (0:p-1);\n", "subplot(2,1,2);\n", "plot(t(sel),x(sel)); axis tight;"]}, {"source": ["__Exercise 1__\n", "\n", "Compute the local Fourier transform around a point |t0| of |x|, which is the FFT (use the\n", "function |fft|) of the windowed signal |x.*h| where |h| is smooth\n", "windowing function located around |t0|. For instance you can use for |h|\n", "a Gaussian bump centered at |t0|. To center the FFT for display, use\n", "|fftshift|.\n", "enter for the Fourier analysis\n", "idth of the bump\n", "indow\n", "ft\n", "isplay signal\n", "isplay FFTs"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 8, "cell_type": "code", "language": "python", "metadata": {}, "input": ["exo1()"]}, {"collapsed": false, "outputs": [], "prompt_number": 9, "cell_type": "code", "language": "python", "metadata": {}, "input": ["%% Insert your code here."]}, {"source": ["A good windowing function should balance both time localization and\n", "frequency localization."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 10, "cell_type": "code", "language": "python", "metadata": {}, "input": ["t = linspace(-10,10,2048);\n", "eta = 1e-5;\n", "vmin = -2;"]}, {"source": ["The block window has a sharp transition and thus a poor frequency\n", "localization."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 11, "cell_type": "code", "language": "python", "metadata": {}, "input": ["h = double( abs(t)<1 );\n", "hf = fftshift(abs(fft(h)));\n", "hf = log10(eta+hf); hf = hf/max(hf);\n", "clf;\n", "subplot(2,1,1);\n", "title('Block window');\n", "plot(t, h); axis([-2 2, -.1, 1.1]);\n", "subplot(2,1,2);\n", "plot(t, hf); axis([-2 2, vmin, 1.1]);\n", "title('Fourier transform');"]}, {"source": ["A Hamming window is smoother."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 12, "cell_type": "code", "language": "python", "metadata": {}, "input": ["h = cos(t*pi()/2) .* double(abs(t)<1);\n", "hf = fftshift(abs(fft(h)));\n", "hf = log10(eta+hf); hf = hf/max(hf);\n", "clf;\n", "subplot(2,1,1);\n", "title('Hamming window');\n", "plot(t, h); axis([-2 2, -.1, 1.1]);\n", "subplot(2,1,2);\n", "plot(t, hf); axis([-2 2, vmin, 1.1]);\n", "title('Fourier transform');"]}, {"source": ["A Haning window has continuous derivatives."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 13, "cell_type": "code", "language": "python", "metadata": {}, "input": ["h = (cos(t*pi())+1)/2 .* double(abs(t)<1);\n", "hf = fftshift(abs(fft(h)));\n", "hf = log10(eta+hf); hf = hf/max(hf);\n", "clf;\n", "subplot(2,1,1);\n", "title('Haning window');\n", "plot(t, h); axis([-2 2, -.1, 1.1]);\n", "subplot(2,1,2);\n", "plot(t, hf); axis([-2 2, vmin, 1.1]);\n", "title('Fourier transform');"]}, {"source": ["A normalized Haning window has a sharper transition. It has the advantage\n", "of generating a tight frame STFT, and is used in the following."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 14, "cell_type": "code", "language": "python", "metadata": {}, "input": ["h = sqrt(2)/2 * (1+cos(t*pi())) ./ sqrt( 1+cos(t*pi()).^2 ) .* double(abs(t)<1);\n", "hf = fftshift(abs(fft(h)));\n", "hf = log10(eta+hf); hf = hf/max(hf);\n", "clf;\n", "subplot(2,1,1);\n", "title('Normalized Haning window');\n", "plot(t, h); axis([-2 2, -.1, 1.1]);\n", "subplot(2,1,2);\n", "plot(t, hf); axis([-2 2, vmin, 1.1]);\n", "title('Fourier transform');"]}, {"source": ["Short time Fourier transform.\n", "-----------------------------\n", "Gathering a local Fourier transform at equispaced point create a local\n", "Fourier transform, also called *spectrogram*. By carefully chosing the\n", "window, this transform corresponds to the decomposition of the signal in\n", "a redundant tight frame. The redundancy corresponds to the overlap of the\n", "windows, and the tight frame corresponds to the fact that the\n", "pseudo-inverse is simply the transposed of the transform (it means that\n", "the same window can be used for synthesis with a simple summation of the\n", "reconstructed signal over each window).\n", "\n", "\n", "The only parameters of the transform are the size of the window and the\n", "overlap.\n", "\n", "size of the window"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 15, "cell_type": "code", "language": "python", "metadata": {}, "input": ["w = 64*2;"]}, {"source": ["overlap of the window"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 16, "cell_type": "code", "language": "python", "metadata": {}, "input": ["q = w/2;"]}, {"source": ["Gabor atoms are computed using a Haning window.\n", "The atoms are obtained by translating in time and in frequency\n", "(modulation) the window."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 17, "cell_type": "code", "language": "python", "metadata": {}, "input": ["t = 0:3*w-1;\n", "t1 = t-2*w;\n", "f = w/8;"]}, {"source": ["Position 0, frequency 0."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 18, "cell_type": "code", "language": "python", "metadata": {}, "input": ["g1 = sin( pi*t/w ).^2 .* double(t=0 );"]}, {"source": ["Position 0, frequency w/8"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 20, "cell_type": "code", "language": "python", "metadata": {}, "input": ["g3 = g1 .* sin( t * 2*pi/w * f);"]}, {"source": ["Position 2*w, frequency w/8"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 21, "cell_type": "code", "language": "python", "metadata": {}, "input": ["g4 = g2 .* sin( t * 2*pi/w * f);"]}, {"source": ["display"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 22, "cell_type": "code", "language": "python", "metadata": {}, "input": ["clf;\n", "subplot(2,2,1);\n", "plot(g1); axis('tight');\n", "title('Position 0, frequency 0');\n", "subplot(2,2,2);\n", "plot(g2); axis('tight');\n", "title('Position 2*w, frequency 0');\n", "subplot(2,2,3);\n", "plot(g3); axis('tight');\n", "title('Position 0, frequency w/8');\n", "subplot(2,2,4);\n", "plot(g4); axis('tight');\n", "title('Position 2*w, frequency w/8');"]}, {"source": ["We can compute a spectrogram of the sound to see its local Fourier\n", "content. The number of windowed used is |(n-noverlap)/(w-noverlap)|"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 23, "cell_type": "code", "language": "python", "metadata": {}, "input": ["S = perform_stft(x,w,q, options);"]}, {"source": ["To see more clearly the evolution of the harmonics, we can display the\n", "spectrogram in log coordinates. The top of the spectrogram corresponds to\n", "low frequencies.\n", "\n", "display the spectrogram"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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fLBvf5b6CoriyUQxDW3Q1wsLP8/z8vC1/CUdvM9QvY2Njk5OTZvbgwQMzO3Lk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\nY9YNRZpad/XqVVWGFKtkfn4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"metadata": {}, "output_type": "display_data"}], "prompt_number": 24, "cell_type": "code", "language": "python", "metadata": {}, "input": ["clf; imageplot(abs(S)); axis('on');"]}, {"source": ["display log spectrogram"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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gfCQWGRwKyrwAu4fAlK/I71vL/Oe+ZuFxUJsDuL6SBj4q9EXt1UgoAVxrZJpd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"metadata": {}, "output_type": "display_data"}], "prompt_number": 25, "cell_type": "code", "language": "python", "metadata": {}, "input": ["plot_spectrogram(S,x);"]}, {"source": ["The STFT transform is decomposing the signal in a redundant tight frame.\n", "This can be checked by measuring the energy conservation.\n", "\n", "energy of the signal"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 26, "cell_type": "code", "language": "python", "metadata": {}, "input": ["e = norm(x,'fro').^2;"]}, {"source": ["energy of the coefficients"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"metadata": {}, "text": ["Energy conservation (should be 1)=1\n"], "output_type": "display_data"}], "prompt_number": 27, "cell_type": "code", "language": "python", "metadata": {}, "input": ["eS = norm(abs(S),'fro').^2;\n", "disp(strcat(['Energy conservation (should be 1)=' num2str(e/eS)]));"]}, {"source": ["One can also\n", "check that the inverse transform (which is just the transposed operator -\n", "it implements exactly the pseudo inverse) is working fine.\n", "\n", "one must give the signal size for the reconstruction"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"metadata": {}, "text": ["Reconstruction error (should be 0)=2.2401e-16\n"], "output_type": "display_data"}], "prompt_number": 28, "cell_type": "code", "language": "python", "metadata": {}, "input": ["x1 = perform_stft(S,w,q, options);\n", "disp(strcat(['Reconstruction error (should be 0)=' num2str( norm(x-x1, 'fro')./norm(x,'fro') ) ]));"]}, {"source": ["Audio Denoising\n", "---------------\n", "One can perform denosing by a non-linear thresholding over the\n", "transfomede Fourier domain.\n", "\n", "\n", "First we create a noisy signal"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 29, "cell_type": "code", "language": "python", "metadata": {}, "input": ["sigma = .2;\n", "xn = x + randn(size(x))*sigma;"]}, {"source": ["Play the noisy sound."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 30, "cell_type": "code", "language": "python", "metadata": {}, "input": ["sound(xn,fs);"]}, {"source": ["Display the Sounds."], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 31, "cell_type": "code", "language": "python", "metadata": {}, "input": ["clf;\n", "subplot(2,1,1);\n", "plot(x); axis([1 n -1.2 1.2]);\n", "set_graphic_sizes([], 20);\n", "title('Original signal');\n", "subplot(2,1,2);\n", "plot(xn); axis([1 n -1.2 1.2]);\n", "set_graphic_sizes([], 20);\n", "title('Noisy signal');"]}, {"source": ["One can threshold the spectrogram.\n", "\n", "perform thresholding"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 32, "cell_type": "code", "language": "python", "metadata": {}, "input": ["Sn = perform_stft(xn,w,q, options);\n", "SnT = perform_thresholding(Sn, 2*sigma, 'hard');"]}, {"source": ["display the results"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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npwhcPXxM7iBZWWlLXcEn9F1lsDT1EsgOkSpf4m/2aJeeA6DmOi5OrHD1HrDk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\ntQAAAABJRU5ErkJggg==\n", "metadata": {}, "output_type": "display_data"}], "prompt_number": 33, "cell_type": "code", "language": "python", "metadata": {}, "input": ["subplot(2,1,1);\n", "plot_spectrogram(Sn);\n", "subplot(2,1,2);\n", "plot_spectrogram(SnT);"]}, {"source": ["__Exercise 2__\n", "\n", "A denoising is performed by hard or soft thresholding the STFT of the\n", "noisy signal. Compute the denosing SNR with both soft and hard\n", "thresholding, and compute the threshold that minimize the SNR. Remember that a soft thresholding\n", "should be approximately twice smaller than a hard thresholding. Check the\n", "result by listening. What can you conclude about the quality of the\n", "denoised signal ?\n", "etrieve best hard thresholding result\n", "isplay the error curves"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 34, "cell_type": "code", "language": "python", "metadata": {}, "input": ["exo2()"]}, {"collapsed": false, "outputs": [], "prompt_number": 35, "cell_type": "code", "language": "python", "metadata": {}, "input": ["%% Insert your code here."]}, {"source": ["__Exercise 3__\n", "\n", "Display and hear the results. What do you notice ?\n", "isplay\n", "ear"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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YACEJMABCEmAAhCTAAAhJgAEQkgADICQBBkBIAgyAkAQYACEJMABCEmAAhCTA\nAAhJgAEQkgADICQBBkBIAgyAkAQYACEJMABCEmAAhCTAAAhJgAEQkgADICQBBkBIAgyAkP4PO6bM\nCOPEIBAAAAAASUVORK5CYII=\n", "metadata": {}, "output_type": "display_data"}], "prompt_number": 36, "cell_type": "code", "language": "python", "metadata": {}, "input": ["exo3()"]}, {"collapsed": false, "outputs": [], "prompt_number": 37, "cell_type": "code", "language": "python", "metadata": {}, "input": ["%% Insert your code here."]}, {"source": ["Audio Block Thresholding\n", "------------------------\n", "It is possible to remove musical noise by thresholding blocks of STFT\n", "coefficients.\n", "\n", "\n", "Denoising is performed by block soft thresholding.\n", "\n", "perform thresholding"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [], "prompt_number": 38, "cell_type": "code", "language": "python", "metadata": {}, "input": ["Sn = perform_stft(xn,w,q, options);\n", "SnT = perform_thresholding(Sn, sigma, 'block');"]}, {"source": ["display the results"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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npwhcPXxM7iBZWWlLXcEn9F1lsDT1EsgOkSpf4m/2aJeeA6DmOi5OrHD1HrDk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\n0KgPbttIzgKUhrZ+LLRx03PU/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"metadata": {}, "output_type": "display_data"}], "prompt_number": 39, "cell_type": "code", "language": "python", "metadata": {}, "input": ["subplot(2,1,1);\n", "plot_spectrogram(Sn);\n", "subplot(2,1,2);\n", "plot_spectrogram(SnT);"]}, {"source": ["__Exercise 4__\n", "\n", "Trie for various block sizes and report the best results.\n", " progressbar(k,length(bsX(:)));"], "metadata": {}, "cell_type": "markdown"}, {"collapsed": false, "outputs": [{"png": 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"metadata": {}, "output_type": "display_data"}], "prompt_number": 40, "cell_type": "code", "language": "python", "metadata": {}, "input": ["exo4()"]}, {"collapsed": false, "outputs": [], "prompt_number": 41, "cell_type": "code", "language": "python", "metadata": {}, "input": ["%% Insert your code here."]}], "metadata": {}}], "nbformat": 3, "metadata": {"kernelspec": {"name": "matlab_kernel", "language": "matlab", "display_name": "Matlab"}, "language_info": {"mimetype": "text/x-matlab", "name": "matlab", "file_extension": ".m", "help_links": [{"url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md", "text": "MetaKernel Magics"}]}}}