Spectral Envelope ExamplesThis section presents matlab code for computing spectral envelopes by the cepstral and linear prediction methods discussed above. The signal to be modeled is a synthetic ``ah'' vowel (as in ``father'') synthesized using three formants driven by a bandlimited impulse train .
% Specify formant resonances for an "ah" [a] vowel: F = [700, 1220, 2600]; % Formant frequencies in Hz B = [130, 70, 160]; % Formant bandwidths in Hz fs = 8192; % Sampling rate in Hz % ("telephone quality" for speed) R = exp(-pi*B/fs); % Pole radii theta = 2*pi*F/fs; % Pole angles poles = R .* exp(j*theta); [B,A] = zp2tf(0,[poles,conj(poles)],1); f0 = 200; % Fundamental frequency in Hz w0T = 2*pi*f0/fs; nharm = floor((fs/2)/f0); % number of harmonics nsamps = fs; % make a second's worth sig = zeros(1,nsamps); n = 0:(nsamps-1); % Synthesize bandlimited impulse train: for i=1:nharm, sig = sig + cos(i*w0T*n); end; sig = sig/max(sig); soundsc(sig,fs); % Let's hear it % Now compute the speech vowel: speech = filter(1,A,sig); soundsc([sig,speech],fs); % "buzz", "ahh" % (it would sound much better with a little vibrato)The Hamming-windowed bandlimited impulse train sig and its spectrum are plotted in Fig.10.1. 10.2 shows the Hamming-windowed synthesized vowel speech, and its spectrum overlaid with the true formant envelope.
Spectral Envelope by the Cepstral Windowing MethodWe now compute the log-magnitude spectrum, perform an inverse FFT to obtain the real cepstrum, lowpass-window the cepstrum, and perform the FFT to obtain the smoothed log-magnitude spectrum:
Nframe = 2^nextpow2(fs/25); % frame size = 40 ms w = hamming(Nframe)'; winspeech = w .* speech(1:Nframe); Nfft = 4*Nframe; % factor of 4 zero-padding sspec = fft(winspeech,Nfft); dbsspecfull = 20*log(abs(sspec)); rcep = ifft(dbsspecfull); % real cepstrum rcep = real(rcep); % eliminate round-off noise in imag part period = round(fs/f0) % 41 nspec = Nfft/2+1; aliasing = norm(rcep(nspec-10:nspec+10))/norm(rcep) % 0.02 nw = 2*period-4; % almost 1 period left and right if floor(nw/2) == nw/2, nw=nw-1; end; % make it odd w = boxcar(nw)'; % rectangular window wzp = [w(((nw+1)/2):nw),zeros(1,Nfft-nw), ... w(1:(nw-1)/2)]; % zero-phase version wrcep = wzp .* rcep; % window the cepstrum ("lifter") rcepenv = fft(wrcep); % spectral envelope rcepenvp = real(rcepenv(1:nspec)); % should be real rcepenvp = rcepenvp - mean(rcepenvp); % normalize to zero meanFigure 10.3 shows the real cepstrum of the synthetic ``ah'' vowel (top) and the same cepstrum truncated to just under a period in length. In theory, this leaves only formant envelope information in the cepstrum. Figure 10.4 shows an overlay of the spectrum, true envelope, and cepstral envelope.
Spectral Envelope by Linear PredictionFinally, let's do an LPC window. It had better be good because the LPC model is exact for this example.
M = 6; % Assume three formants and no noise % compute Mth-order autocorrelation function: rx = zeros(1,M+1)'; for i=1:M+1, rx(i) = rx(i) + speech(1:nsamps-i+1) ... * speech(1+i-1:nsamps)'; end % prepare the M by M Toeplitz covariance matrix: covmatrix = zeros(M,M); for i=1:M, covmatrix(i,i:M) = rx(1:M-i+1)'; covmatrix(i:M,i) = rx(1:M-i+1); end % solve "normal equations" for prediction coeffs: Acoeffs = - covmatrix \ rx(2:M+1) Alp = [1,Acoeffs']; % LP polynomial A(z) dbenvlp = 20*log10(abs(freqz(1,Alp,nspec)')); dbsspecn = dbsspec + ones(1,nspec)*(max(dbenvlp) ... - max(dbsspec)); % normalize plot(f,[max(dbsspecn,-100);dbenv;dbenvlp]); grid;
autocorrelation estimate was unbiased). The code should run in either Octave or Matlab with the Signal Processing Toolbox. The Matlab Signal Processing Toolbox has the function lpc available. (LPC stands for ``Linear Predictive Coding.'') The Octave-Forge lpc function (version 20071212) is a wrapper for the lattice function which implements Burg's method by default. Burg's method has the advantage of guaranteeing stability ( is minimum phase) while yielding accuracy comparable to the covariance method. By uncommenting lines in lpc.m, one can instead use the ``geometric lattice'' or classic autocorrelation method (called ``Yule-Walker'' in lpc.m). For details, ``type lpc''.
Additive Synthesis (Early Sinusoidal Modeling)
Linear Prediction Spectral Envelope