How to get mfcc features with octave

Question

My goal is to create program on octave that loads audio file (wav, flac), calculates its mfcc features and serve them as output. The problem is that I do not have much experience with octave and cannot get octave load the audio file and that is why I am not sure if the extraction algorithms is correct. Is there simple way of loading the file and getting its features?

Answer 1

You can run mfcc code from RASTAMAT in octave, you only need to fix few things, the fixed version is available for download here.

The changes are to properly set windows in powspec.m

  WINDOW = hanning(winpts);

and to fix the bug in specgram function which is not compatible with Matlab.

Answer 2

Check out Octave functions for calculating MFCC at https://github.com/jagdish7908/mfcc-octave

For a detailed theory on steps to compute MFCC, refer http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/

 function frame = create_frames(y, Fs, Fsize, Fstep)
  N = length(y);
  % divide the signal into frames with overlap = framestep
  samplesPerFrame = floor(Fs*Fsize);
  samplesPerFramestep = floor(Fs*Fstep);
  i = 1;
  frame = [];
  while(i <= N-samplesPerFrame)
    frame = [frame y(i:(i+samplesPerFrame-1))];
    i = i+samplesPerFramestep;
  endwhile
  return 
 endfunction

function ans = hz2mel(f)
  ans = 1125*log(1+f/700);
  return
 endfunction

 function ans = mel2hz(f)
  ans = 700*(exp(f/1125) - 1);
  return
 endfunction

function bank = melbank(n, min, max, sr)
  % n = number of banks
  % min = min frequency in hertz
  % max = max frequency in hertz 
  % convert the min and max freq in mel scale
  NFFT = 512;
  % figure out bin value of min and max freq
  minBin = floor((NFFT)*min/(sr/2));
  maxBin = floor((NFFT)*max/(sr/2));
  % convert the min, max in mel scale
  min_mel = hz2mel(min);
  max_mel = hz2mel(max);
  m = [min_mel:(max_mel-min_mel)/(n+2-1):max_mel];
  %disp(m);
  h = mel2hz(m);
  % replace frequencies in h with thier respective bin values
  fbin = floor((NFFT)*h/(sr/2));

  %disp(h);
  % create triangular melfilter vectors
  H = zeros(NFFT,n);
  for vect = 2:n+1
    for k = minBin:maxBin
      
      if k >= fbin(vect-1) && k <= fbin(vect)
        H(k,vect) = (k-fbin(vect-1))/(fbin(vect)-fbin(vect-1));  
      elseif k >= fbin(vect) && k <= fbin(vect+1)
        H(k,vect) = (fbin(vect+1) - k)/(fbin(vect+1)-fbin(vect));
      endif
      
    endfor
  endfor
  bank = H;
  return
 endfunction     

clc;
clear all;
close all;
pkg load signal;

% record audio
Fs = 44100;
y = record(3,44100);
% OR %
% Load existing file
%[y, Fs] = wavread('../FILE_PATH/');
%y = y(44100:2*44100);
 
 % create mel filterbanks
 minFreq = 500;   % minimum cutoff frequency in Hz
 maxFreq = 10000;   % maximum cutoff frequency in Hz
% melbank(number_of_banks, minFreq, mazFreq, sampling_rate)
 foo = melbank(30,minFreq,maxFreq,Fs);

 % create frames
 frames = create_frames(y, Fs, 0.025, 0.010);
 % calculate periodogram of each frame
 NF = length(frames(1,:));
 [P,F] = periodogram(frames(:,1),[], 1024, Fs);
 % apply mel filters to the power spectra
 P = foo.*P(1:512);
 % sum the energy in each filter and take the logarithm
 P = log(sum(P));
 % take the DCT of the log filterbank energies
 % discard the first coeff 'cause it'll be -Inf after taking log
 L = length(P);
 P = dct(P(2:L));
 PXX = P;

 for i = 2:NF
  P = periodogram(frames(:,i),[], 1024, Fs);
   % apply mel filters to the power spectra
  P = foo.*P(1:512);
  % sum the energy in each filter and take the logarithm
  P = log(sum(P));
  % take the DCT of the log filterbank energies
  % discard the first coeff 'cause it'll be -Inf after taking log
  P = dct(P(2:L));
  % coeffients are stacked row wise for each frame
  PXX = [PXX; P];
 endfor
 % stack the coeffients column wise
 PXX = PXX';
 plot(PXX);