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  1. PO
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    <p>First, you have to split the signal in small frames with 10 to 30ms, apply a windowing function (humming is recommended for sound applications), and compute the fourier transform of the signal. With DFT, to compute Mel Frequecy Cepstral Coefficients you have to follow these steps:</p> <ol> <li>Get power spectrum: |DFT|^2</li> <li>Compute a triangular bank filter to transform hz scale into mel scale</li> <li>Get log spectrum</li> <li>Apply discrete cossine transform</li> </ol> <p>A python code example:</p> <pre><code>import numpy from scipy.fftpack import dct from scipy.io import wavfile sampleRate, signal = wavfile.read("file.wav") numCoefficients = 13 # choose the sive of mfcc array minHz = 0 maxHz = 22.000 complexSpectrum = numpy.fft(signal) powerSpectrum = abs(complexSpectrum) ** 2 filteredSpectrum = numpy.dot(powerSpectrum, melFilterBank()) logSpectrum = numpy.log(filteredSpectrum) dctSpectrum = dct(logSpectrum, type=2) # MFCC :) def melFilterBank(blockSize): numBands = int(numCoefficients) maxMel = int(freqToMel(maxHz)) minMel = int(freqToMel(minHz)) # Create a matrix for triangular filters, one row per filter filterMatrix = numpy.zeros((numBands, blockSize)) melRange = numpy.array(xrange(numBands + 2)) melCenterFilters = melRange * (maxMel - minMel) / (numBands + 1) + minMel # each array index represent the center of each triangular filter aux = numpy.log(1 + 1000.0 / 700.0) / 1000.0 aux = (numpy.exp(melCenterFilters * aux) - 1) / 22050 aux = 0.5 + 700 * blockSize * aux aux = numpy.floor(aux) # Arredonda pra baixo centerIndex = numpy.array(aux, int) # Get int values for i in xrange(numBands): start, centre, end = centerIndex[i:i + 3] k1 = numpy.float32(centre - start) k2 = numpy.float32(end - centre) up = (numpy.array(xrange(start, centre)) - start) / k1 down = (end - numpy.array(xrange(centre, end))) / k2 filterMatrix[i][start:centre] = up filterMatrix[i][centre:end] = down return filterMatrix.transpose() def freqToMel(freq): return 1127.01048 * math.log(1 + freq / 700.0) def melToFreq(mel): return 700 * (math.exp(mel / 1127.01048) - 1) </code></pre> <p>This code is based on <a href="https://code.soundsoftware.ac.uk/projects/vampy/repository/revisions/d56f48aafb99/entry/Example%20VamPy%20plugins/PyMFCC.py" rel="nofollow noreferrer">MFCC Vamp example</a>. I hope this help you!</p>
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    1. COHi, Do you mean for "file.wav" to be a frame (10ms to 30ms)? If not, you need to split signal into small frames and then apply the operations you did to each frame. For each frame, you should get out 13 coefficients.
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    2. CO... I was confused with that too. I assumed he was talking about the size of the window. It's Where we grab the values and then compute the FFT on it. Please confirm
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    3. CObut what happens once i have the coefficients? what do it do with them? im assuming i get the coefficients of sound one and then the coefficients of sound 2... then what
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