MFCC matlab code

%function ccc=mfcc(x)   

%归一化mel滤波器组系数   

filename=input(‘input filename:‘,‘s‘);

[x,fs,bits]=wavread(filename);

bank=melbankm(24,256,fs,0,0.5,‘m‘);   

bank=full(bank);   

bank=bank/max(bank(:));   

?T系数，12*24   

for k=1:12   

    n=0:23;   

    dctcoef(k,:)=cos((2*n+1)*k*pi/(2*24));   

end   

%归一化倒谱提升窗口   

w=1+6*sin(pi*[1:12] ./12);   

w=w/max(w);   

%预加重滤波器   

xx=double(x);   

xx=filter([1 -0.9375],1,xx);   

%语音信号分帧   

xx=enframe(xx,256,80);   

%计算每帧的MFCC参数   

for i=1:size(xx,1)   

    y=xx(i,:)   

    s=y‘ .*hamming(256);   

    t=abs(fft(s));   

    t=t.^2;   

    c1=dctcoef*log(bank*t(1:129));   

    c2=c1.*w‘;   

    m(i,:)=c2‘;   

end   

%差分参数   

dtm=zeros(size(m));   

for i=3:size(m,1)-2   

    dtm(i,:)=-2*m(i-2,:)-m(i-1,:)+m(i+1,:)+2*m(i+2,:);   

end   

dtm=dtm/3;   

%合并mfcc参数和一阶差分mfcc参数   

ccc=[m dtm];   

%去除首位两帧，因为这两帧的一阶差分参数为0   

ccc=ccc(3:size(m,1)-2,:);

subplot(211)

ccc_1=ccc(:,1);

plot(ccc_1);title(‘MFCC‘);

% ylabel(‘幅值‘);

% title(‘一维数组及其幅值的关系‘)

% [h,w]=size(ccc);

% A=size(ccc);

% subplot(212)

% plot([1,w],A);

% xlabel(‘维数‘);

% ylabel(‘幅值‘);

% title(‘维数于幅值的关系‘)

return