| dc.description.abstract | In speech coding, it is known that the Differential Pulse
Code Modulation (DPCM) schemes lead to the reduction of bandwidth
requirement (by removing the redundancy) as compared to pulse
Code Modulation (PCM) schemes, for a specified speech quality.
To achieve adequate dynamic range and good subjective quality,
however, it is necessary to adapt the parameters of the quantizer
and predictor to the input signal characteristics. Several
algorithms have been developed for adapting the quantizer and
predictor, most of which suffer from high computational
complexity and are not easily implementable. The study reported
in this thesis is concerned with the development of adaptive PCM
and DPCM (APCM and ADPCM) systems which are based on
computationally simple adaptive quantization and prediction
algorithms.
Two points are noteworthy: firstly, variance estimation of
the quantizer input forms the basis of adaptive quantization and
secondly, in predictor adaptation using the Least Mean Square
gradient (LMS) algorithm it is the estimation of the
reconstructed signal power which is important. Hence, it is
possible to reduce the computational complexity of the APCM and
ADPCM systems by resorting to computationally simple power
estimation schemes. This is achieved here by implementing the
adaptation algorithms for the quantizer and predictor using the
Exponential power Estimation Technique (EPE), which is a modified
form of an existing power estimation method. The estimation
using EPE avoids multiplications and divisions and it expresses
the power estimate in the form 2^, where i is an integer. Hence
the use of EPE leads to a significant reduction in computational
complexity.
Comparative studies of EPE with two conventional power
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estimation schemes (Block and Recursive power estimators: BPE and
RPE) have been presented in the context of step response,
spectral parameter identification and adaptive prediction of
speech. The results indicate that EPE is much superior to RPE
and identical to BPE in performance. However,' the performance of
EPE is limited by the finite number of distinct power estimate
values which the EPE estimate is allowed to take.
The adaptive quantizer used in the APCM and ADPCM systems is
of the hybrid type. EPE is used to estimate the instantaneous
and syllabic standard deviations of the quantizer input signal,
for the purpose of adaptation. In order to make the quantizer
robust to transmission errors, a leakage mechanism has been
introduced in the EPE algorithm, performance evaluation study of
these systems have been done under noise free and noisy channel
conditions using uniform and nonuniform quantizing
characteristics at different bit rates. The input signals used
are sinusoidal, correlated Gaussian sequence and digitized
speech. segmented signal to noise ratio is the performance
measure used.
The APCM systems simulated at bit rates 24 and 32 kbps using
the EPE-quantizer (EPEQ) is found to provide almost identical
performance as that of jayant's adaptive quantizer for speech
input while the performance degrades for sinusoidal input (due to
the speech specific design of EPEQ). A detailed performance
evaluation of the ADPCM system, whose quantizer and predictor are
adapted based on EPE, led to the conclusion that the EPE based
ADPCM is best suited for low bit rate applications. At 16 kbps,
this system outperformed the two conventional ADPCM systems
considered for comparison purpose, with the computational
advantage of having (3N + B + 1) multiplications and one division
compared to the first system and (B + 1) multiplications compared
to the second system, where N is the predictor order and B is the
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number of bits/sample. However, EPE requires one extra
exponentiation operation. Further, modifications are done to
improve the quantizer performance. These include introduction of
additional step sizes in the quantizer and modifications aimed at
improving the dynamic range and noisy channel performances.
The thesis has been organized in the following manner: The
significance of a computationally simple power estimation scheme
in the context of adaptive quantization and prediction is brought
out in chapter 1. This chapter also presents a review of those
speech digitization methods which are relevant to the topic of
this thesis. A detailed introduction and study of the
Exponential power Estimation technique is given in chapter 2.
The application of EPE in adaptive quantization is dealt with in
chapter 3. In Chapter 4, the development of a computationally
simple ADPCM system (16 kbps) which makes use of EPE for adapting
its quantizer and predictor is considered. The results are
summarized in chapter 5. The thesis concludes with some
suggestions for further work in this- area. | |
