A comparison between joe.dat and joe_err.dat is given
in figure 5.6.
Figure 5.6. Linear prediction of a signal
a) First 32000 samples of joe.dat
Sample
number (×
104)
b) First 32000 samples of joe_err.dat
Sample
number (×
104)
c) First 32000 samples of joe_lpc.dat
Sample
number (×
104)
Note that the residual, in figure 5.6 (b), is a lower-amplitude signal than
the original. The prediction residual has sharp spikes at regular intervals,
corresponding to the glottal pulses of the speech wave. Those spikes occur
in the prediction residual at those points in the speech wave at which the
signal is changing direction in a very extreme fashion. At those points, the
linear prediction idea does not work very well, and we get a big error. As
the spacing of those spikes occurs at the fundamental frequency of the original
speech, the occurrence of a spike in the prediction residual is again highly
correlated with the location of spikes on previous cycles. So by estimating
the pitch, we could remove (or at least reduce) the spikes from the prediction
residual. Practically all that is left in the prediction residual after pitch
prediction is just residual noise.
