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1 function [mfccFilterWeights mfccDCTMatrix] = initMFCCVariables(sampleRate, FFTsize)
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2
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3 Fs = sampleRate;
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4 Nfft = FFTsize;
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5
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6 %Filter bank parameters
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7 lowestFrequency = 133.3333;
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8 linearFilters = 13;
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9 linearSpacing = 66.66666666;
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10 logFilters = 27;
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11 logSpacing = 1.0711703;
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12 cepstralCoefficients = 13;
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13
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14 % Keep this around for later....
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15 totalFilters = linearFilters + logFilters;
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16
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17 % Now figure the band edges. Interesting frequencies are spaced
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18 % by linearSpacing for a while, then go logarithmic. First figure0
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19 % all the interesting frequencies. Lower, center, and upper band
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20 % edges are all consecutive interesting frequencies.
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21 freqs = lowestFrequency + (0:linearFilters-1)*linearSpacing;
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22 freqs(linearFilters+1:totalFilters+2) = freqs(linearFilters) * logSpacing.^(1:logFilters+2);
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23 lower = freqs(1:totalFilters);
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24 center = freqs(2:totalFilters+1);
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25 upper = freqs(3:totalFilters+2);
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26
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27 % each filter has unit weight, assuming a triangular weighting function
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28 mfccFilterWeights = zeros(totalFilters,Nfft/2);
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29 triangleHeight = 2./(upper-lower);
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30 fftFreqs = (0:Nfft/2-1)/(Nfft)*Fs;
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31 for chan=1:totalFilters
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32 mfccFilterWeights(chan,:) = (fftFreqs > lower(chan) & fftFreqs <= center(chan)).* triangleHeight(chan).*(fftFreqs-lower(chan))/(center(chan)-lower(chan)) + ...
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33 (fftFreqs > center(chan) & fftFreqs < upper(chan)) .* triangleHeight(chan).*(upper(chan)-fftFreqs)/(upper(chan)-center(chan));
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34 end
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35
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36 % Figure out Discrete Cosine Transform. We want a matrix
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37 % dct(i,j) which is totalFilters x cepstralCoefficients in size.
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38 % The i,j component is given by cos( i * (j+0.5)/totalFilters pi )
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39 % where we have assumed that i and j start at 0.
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40 mfccDCTMatrix = 1/sqrt(totalFilters/2)*cos((0:(cepstralCoefficients-1))' * (2*(0:(totalFilters-1))+1) * pi/2/totalFilters);
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41 mfccDCTMatrix(1,:) = mfccDCTMatrix(1,:) * sqrt(2)/2;
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42
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