I am trying to implement MFCC by following this and I found that there is a step in which signals are divided into frames and then passed on to Hamming windowing process. The reason given there is to correct the discontinuity at the start and last samples of the frame. OK that sounds good, then the output of the window is fed for FFT process. So I thought of doing this sample code separately.
%Creating a signal to perform hamming windowing on. fs=8000; ts=1/fs; N=1000; t=(0:ts:(N-1)*ts); x=sin(2*pi*60*t); figure,subplot(221),plot(t,x,'.-'),title('Original Speech'),xlabel('Time');grid on f=(-fs/2:fs/(N-1):fs/2); subplot(223),plot(f,fftshift(abs(fft(x)))),title('Frequency Spectrum'),xlabel('Freq (Hz)');grid on windowed=x.*hamming(length(x))'; subplot(222),plot(t,windowed,'.-'),title('Hamming Window Applied'),xlabel('Time');grid on subplot(224),plot(f,fftshift(abs(fft(windowed)))),title('Frequency Spectrum After Hamming'),xlabel('Freq (Hz)');grid on
The plot generated is:-
My Problem is:-
What is the advantage of Hamming Windowing. I am a noob, so according to me, firstly we are loosing the amplitude at the staring and ending of the signal in the time domain and secondly, in the frequency domain, there is very little difference. So what is the advantage of doing all this much computation. Please explain with as much detail as possible.
And it would be nice if someone could point out to more detailed (according to them) tutorial then what I am referencing or what they read while learning MFCC themselves.