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622 Chapter 12 APPLICATIONS IN COMMUNICATIONS
DM, the interested reader is referred to the papers by Jayant [14] and
Flanagan et al. [4] and to the extensive references contained in these
papers.
12.4.2 PROJECT 12.4: DM AND ADM
The purpose of this project is to gain an understanding of delta modulation
and adaptive delta modulation for coding of waveforms. This project
involves writing MATLAB functions for the DM encoder and decoder as
shown in Figure 12.9, and for the ADM encoder and decoder shown in
Figure 12.11. The lowpass filter at the decoder can be implemented as a
linear-phase FIR filter. For example, a Hanning filter that has the impulse
response
h(n) = 1 [ ( )] 2πn
1 − cos
, 0 ≤ n ≤ N − 1 (12.26)
2 N − 1
may be used, where the length N may be selected in the range 5 ≤ N ≤ 15.
The input to the DM and ADM systems can be supplied from the
waveforms generated in Project 12.1 except that the sampling rate should
be higher by a factor of 5 to 10. The output of the decoder can be plotted.
Comparisons should be made between the output signal from the DM and
ADM decoders and the original input signal.
12.5 LINEAR PREDICTIVE CODING (LPC) OF SPEECH
The linear predictive coding (LPC) method for speech analysis and synthesis
is based on modeling the vocal tract as a linear all-pole (IIR) filter
having the system function
H (z) =
1+
G
(12.27)
p∑
a p (k) z −k
k=1
where p is the number of poles, G is the filter gain, and {a p (k)} are the
parameters that determine the poles. There are two mutually exclusive
excitation functions to model voiced and unvoiced speech sounds. On a
short-time basis, voiced speech is periodic with a fundamental frequency
F 0 ,orapitch period 1/F 0 , which depends on the speaker. Thus voiced
speech is generated by exciting the all-pole filter model by a periodic
impulse train with a period equal to the desired pitch period. Unvoiced
speech sounds are generated by exciting the all-pole filter model by the
output of a random-noise generator. This model is shown in Figure 12.12.
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