cfmfcc.m

用matlab编程,采用神经网络是别的方法,

function r =cfmfcc(s, fs)
% MFCC
%
% Inputs: s  contains the signal to analize
%         fs is the sampling rate of the signal
%
% Output: r contains the transformed signal
%
%
%%%%%%%%%%%%%%%%%%
% Mini-Project: An automatic speaker recognition system
%
% Responsible: Vladan Velisavljevic
% Authors:     Christian Cornaz
%              Urs Hunkeler

m = 80;
n = 160;
l = length(s);

nbFrame = floor((l - n) / m) + 1;

for i = 1:n
    for j = 1:nbFrame
        M(i, j) = s(((j - 1) * m) + i);
    end
end

h = hamming(n);

M2 = diag(h) * M;

for i = 1:nbFrame
    frame(:,i) = fft(M2(:, i));
end

t = n / 2;
tmax = l / fs;

m = melfb(24, n, fs);
n2 = 1 + floor(n / 2);
z = m * abs(frame(1:n2, :)).^2;

r = dct(log(z));
r=r(1:15,:);

%差分系数
d = zeros(size(r));
for i=2:size(r,1)-2
 d(i,:) = r(i+1,:)-r(i-1,:);
end
d = d / 2;

%合并mfcc参数和一阶差分mfcc参数
r = [r d];
%去除首尾两帧，因为这两帧的一阶差分参数为0
r = r(3:size(r,1)-2,:);