sir,I have to do the fourier bessel cepstral coefiicient (FBCC) for an audio to extract the features. how to do it?

I done a code for mfcc. but for FBCC i have to do the fourier bessel coefficient after the windowing (or before mel frequency filter bank). but i dont know how to do it. Can you please help me to complete the code. I will give my code here.

[audio, fs1] = audioread('cryrumble.wav');
%sound(x,fs1);
   ts1=1/fs1;
    N1=length(audio);
    Tmax1=(N1-1)*ts1;
    t1=(0:ts1:Tmax1);
    figure;
  plot(t1,audio),xlabel('Time'),title('Original audio');
  fs2 = (20/441)*fs1;
  y=resample(audio,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');



 %Step 1: Pre-Emphasis
 
 a=[1];
     b=[1 -0.95];
    z=filter(b,a,y);
    subplot(413),plot(t2,z),xlabel('Time'),title('Signal After High Pass Filter - Time Domain');
     subplot(414),plot(fs2,fftshift(abs(fft(z)))),xlabel('Freq (Hz)'),title('Signal After High Pass Filter - Frequency Spectrum');
       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

% Step 2: Frame Blocking

     frameSize=1000;
%     frameOverlap=128;
%     frames=enframe(y,frameSize,frameOverlap);
%     NumFrames=size(frames,1);
frame_duration=0.06;
frame_len = frame_duration*fs2;
framestep=0.01;
framestep_len=framestep*fs2;
% N = length (x);
num_frames =floor(N2/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=z((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(1000)';
windowed = bsxfun(@times, frames, hamm);

      
     % Step 4: FFT 
% Taking only the positive values in the FFT that is the first half of the frame after being computed. 


       ft = abs( fft(windowed,500, 2) );
       plot(ft);
    
% 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
    figure;
    plot(H);
    % 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