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

2 views (last 30 days)
Suchithra K S on 28 Nov 2018
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.
%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
%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
X=i;
k=1;
[IDXi,ci] = kmeans(X,k);
save c41i ci