2008-02-21-lpc.diary

% 2008-02-21-lpc.diary
% Examples of using LPC functions

% Load speech soundfile
[d,r]=wavread('mpgr1_sx419.wav');
% Downsample to 8 kHz (LPC works better with a limited bandwidth)
d = resample(d,1,2);
r = r/2;
soundsc(d,r);

% 8th-order LPC modeling
[a,g,e] = lpcfit(d,8);

% Noise-excited resynthesis
rd = lpcsynth(a,g);
% Listen
soundsc(rd)

% Compare spectrograms
subplot(211)
specgram(d,256,r);
subplot(212)
specgram(rd,256,r);
% Clearly the same broad shape, but without pitch structure.

% Residual-excited resynthesis (i.e. using the 'e' argument)
rde = lpcsynth(a,g,e);
soundsc(rde)
% Perceptually identical to original
% What is the excitation signal like?
% It should contain everything *not* in the noise-excited reconstruction
soundsc(e)
% Weird, flat power in time and frequency, still intelligible!
specgram(e,256,r);
% Interesting that formants still visible, resulting from slight 
% modeling mismatch in the LPC

% Compare original waveform with residual
plot(d)
% Choose a portion straddling fricative-vowel in "football"
subplot(311)
plot(d(2000+[1:1000]))
subplot(312)
plot(rde(2000+[1:1000]))
% Residual-excited resynth is close but not identical due to
% the overlap-adding of adjacent frames
subplot(313)
plot(e(2000+[1:1000]))
% Residual is basically noise during fricatives or silence;
% clearly shows pitch pulses during voicing

% Let's just check the model during this vowel
% Frames occur every 128 samples, so middle of vowel (sample 2500)
% is around frame 20...  Look at the gain (energy) terms to 
% find peak
g(17:23)
%ans =
%    0.0015
%    0.0009
%    0.0053
%    0.0071
%    0.0037
%    0.0003
%    0.0000
% g(20) is the peak.  Let's see the spectrum: reconstruct the polynomial
a20 = a(20,:);
g20 = g(20);
% see the filter response (scale up by energy of one window's excitation)
[h,w]=freqz(g20*sqrt(256), a20);
subplot(211)
plot(w/pi, 20*log10(abs(h)));
% Compare to poles of the IIR filter
subplot(212)
zplane(roots(a20))
% poles close to unit circle corresponding to spectral peaks.

% Compare to spectral slice at that point
% (Include pre-emphasis in spectrogram, to match actual modeled signal)
sg=specgram(filter([1 -0.9], 1, d),256,r);
subplot(212)
plot([0:128]/128, 20*log10(abs(sg(:,20))))
axis([0 1 -40 0])
grid
% LPC traces/approximates general trend of actual spectrum: superimpose it
hold on
plot(w/pi, 20*log10(abs(h)), '-r')
hold off