i have a code for extracting the mfcc feature from a audio of elephant rumble and it is given below,

if true
  % code
end

clear all; close all; % Step 0: Reading the File & initializing the Time and Freq.

[x, fs1] = audioread('audio3.wav'); %sound(x,fs1); ts1=1/fs1; N1=length(x); Tmax1=(N1-1)*ts1; t1=(0:ts1:Tmax1); figure; plot(t1,x),xlabel('Time'),title('Original audio'); fs2 = (20/441)*fs1; y=resample(x,2000,44100); %sound(y,fs2); ts2=1/fs2; N2=length(y); Tmax2=(N2-1)*ts2; t2=(0:ts2:Tmax2); figure; plot(t2,y),xlabel('Time'),title('resampled audio'); % spectrogram(y); nchan = size(y,2); for chan = 1 : nchan %subplot(1, nchan, chan) spectrogram(y(:,chan), 256, [], 25, 2000, 'yaxis'); title( sprintf('spectrogram of resampled audio ' ) ); end

% [x,fs]=audioread('audio2.wav'); % ts=1/fs; % N=length(x); % Tmax=(N-1)*ts; % fsu=fs/(N-1); % t=(0:ts:Tmax); % f=(-fs/2:fsu:fs/2); % figure, subplot(411),plot(t,x),xlabel('Time'),title('Original audio'); % subplot(412),plot(f,fftshift(abs(fft(x)))),xlabel('Freq (Hz)'),title('Frequency Spectrum'); % Step 1: Pre-Emphasis % % a=[1]; % b=[1 -0.95]; % y=filter(b,a,x); % subplot(413),plot(t,y),xlabel('Time'),title('Signal After High Pass Filter - Time Domain'); % subplot(414),plot(f,fftshift(abs(fft(y)))),xlabel('Freq (Hz)'),title('Signal After High Pass Filter - Frequency Spectrum'); % Step 2: Frame Blocking

     frameSize=400;
%     frameOverlap=128;
%     frames=enframe(y,frameSize,frameOverlap);
%     NumFrames=size(frames,1);
frame_duration=0.2;
frame_len = frame_duration*fs2;
framestep=0.1;
framestep_len=framestep*fs2;
N = length (y);
num_frames =floor(N/frame_len);
% new_sig =zeros(N,1);
% count=0;
% frame1 =x(1:frame_len);
% frame2 =x(frame_len+1:frame_len*2);
% frame3 =x(frame_len*2+1:frame_len*3);
frames=[];
for j=1:num_frames
     frame=y((j-1)*framestep_len + 1: ((j-1)*framestep_len)+frame_len);
%     frame=x((j-1)*frame_len +1 :frame_len*j);
%     identify the silence by finding frames with max amplitude less than
%     0.025
max_val=max(frame);
   if (max_val>0.025)
%     count = count+1;
%     new_sig((count-1)*frame_len+1:frame_len*count)=frames;
        frames=[frames;frame];
   end
end
% Step 3: Hamming Windowing

NumFrames=size(frames,1); hamm=hamming(50)'; for i=1:NumFrames windowed(i,:)=frames(i,:).*hamm; end % Step 4: FFT % Taking only the positive values in the FFT that is the first half of the frame after being computed.

    for i=1:NumFrames
    ft(i,:)=abs(fft((windowed(i,:)),400));     
    plot(ft(i,:))
    end

% Step 5: Mel Filterbanks Lower_Frequency = 100; Upper_Frequency = fs2/2; % With a total of 22 points we can create 20 filters. Nofilters=20; lowhigh=[300 fs2/2]; %Here logarithm is of base 'e' lh_mel=1125*(log(1+lowhigh/700)); mel=linspace(lh_mel(1),lh_mel(2),Nofilters+2); melinhz=700*(exp(mel/1125)-1); %Converting to frequency resolution fres=floor(((frameSize)+1)*melinhz/fs2); %Creating the filters for m =2:length(mel)-1 for k=1:frameSize/2 if k<fres(m-1) H(m-1,k) = 0; elseif (k>=fres(m-1)&&k<=fres(m)) H(m-1,k)= (k-fres(m-1))/(fres(m)-fres(m-1)); elseif (k>=fres(m)&&k<=fres(m+1)) H(m-1,k)= (fres(m+1)-k)/(fres(m+1)-fres(m)); elseif k>fres(m+1) H(m-1,k) = 0; end end end %H contains the 20 filterbanks, we now apply it to the %processed signal. for i=1:NumFrames for j=1:Nofilters bankans(i,j)=sum((ft(i,:).*H(j,:)).^2); end end % Step 6: Nautral Log and DCT

% pkg load signal %Here logarithm is of base '10' logged=log10(bankans); for i=1:NumFrames

        mfcc(i,:)=dct2(logged(i,:));
    end
    %plotting the MFCC
    figure 
    hold on
    for i=1:NumFrames
        plot(mfcc(i,1:13));
    end
    hold off
% save c5 mfcc

i= mfcc; save i i load i.mat X=i; k=1; [IDXi,ci] = kmeans(X,k); save c41i ci mfcccombined.m Displaying mfcccombined.m.

this code showing error like this "audimfcc Matrix dimensions must agree.

Error in audimfcc (line 85) windowed(i,:)=frames(i,:).*hamm; " how to rectify this error?