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CELP 语音编码格式介绍
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EECS 651 Project Report
Design of a CELP coder and analysis of various
quantization techniques
Prof. David L. Neuhoff
By:
Awais M. Kamboh
Krispian C. Lawrence
Aditya M. Thomas
Philip I. Tsai
Winter 2005
University of Michigan
Ann Arbor
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Table of Contents
List of Figures
3
Introduction
4
Speech Coding 4
Speech Production
4
Speech Signal
5
Time Domain Representation 5
Frequency Domain Representation 6
Codebook Excited Linear Prediction 6
Required Parameters 8
LP Analysis
8
Perceptual Weighting Filter
8
Excitation sequence
9
Pitch Filter
9
Energy Minimization
9
Quantization
11
CELP Synthesizer 12
Perceptual Filter Revisited
12
Speech Reconstruction
14
Quantization
16
Scalar Quantization 16
Differential Pulse Code Modulation
16
Implementation and performance––SQ and DPCM 17
Results—SQ and DPCM 17
Vector Quantization 19
Codebook Computation 20
Optimality Criteria 20
LBG Algorithm 21
Tree-Structured Vector Quantization
23
Design 23
Results—TSVQ 23
Summary
25
Appendix
26
References
27
2
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List of Figures
Fig. 1 Human generation of speech 5
Fig. 2 basic model of speech production 5
Fig. 3 comparison of voiced and unvoiced speech 6
Fig. 4 Block Diagrams of CELP 7
Fig. 5 Different quantization schemes for different parameters 11
Fig. 6 Block diagram of a CELP synthesizer 12
Fig. 7 Frequency response of perceptual weighting filters 13
Fig. 8 Frequency response of the perceptual filter with different values of c 13
Fig. 9 Waveform of the original and reconstructed speech signals. Only excitation
index and pitch are quantized. 14
Fig. 10 Original and reconstructed waveforms. Only LP coefficients are unquantized. 15
Fig. 11 Performance predicted by Zador’s formula and by experimentation. 18
Fig. 12 the prediction gain of DPCM over SQ at different rates 19
Fig. 13 Original and Reconstructed Speech for TSVQ with Rate = 1.2 24
Fig. 14 TSVQ performance by theory and by experimentation 24
3
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Introduction
Speech Coding
Speech coding has been a common area of research in signal processing since the introduction of
wire-based telephones. Numerous speech coding techniques have been thoroughly researched and
developed, spurned further by the advances in internet, technology and wireless communication.
Speech coding is a fundamental element of digital communications, continuously attracting
attention due to the increase of demands in telecommunication services and capabilities.
Applications of speech coders for signal processing purposes has improved at a very fast pace
throughout the years in order to allow it to take advantage of the increasing capabilities of
communication technology infrastructure and computer hardware.
This project focuses on the area of speech coding. This particular area has become a fundamental
necessity due to the bandwidth limitation of most signal transmission systems. Ideally in speech
coding, a digital representation of a speech signal is coded using a minimum number of bits to
achieve a satisfactory quality of the synthesized signal whilst maintaining a reasonable
computational complexity. Speech coding has two main applications: digital transmission and
storage of speech signals. In speech coding, our aim is to minimize the bit-rate while preserving a
certain quality of speech signal, or to improve speech quality at a certain bit rate.
Currently, there are various kinds of coders being implemented. This project focuses on the
design and implementation of a Code Excited Linear Predictive (CELP) coder. This Linear
Predictive Coding (LPC) method performs LP analysis of speech by extracting the LP parameters
and coefficients and employs a quantization method to search a codebook and compute the
excitation signal. The quantization of the LP parameters, play an important role in the
performance of the CELP coder. This project analyzes the performance of the CELP coder by
using various quantization methods such as Scalar, vector, DPCM and TSVQ to quantize the LP
parameters
.
Speech Production
Before proceeding with the handling of digitized speech, it is crucial to have a basic
understanding of how speech is produced. Speech is produced when the lungs force the direction
of airflow to pass through the larynx into the vocal tract. In normal speech production, the air that
is driven up from the lungs is passed through the glottis and vocal tract narrowing resulting in
periodic or aperiodic (noise) excitation.
Parts of the mouth’s anatomy, such as the jaw, tongue, lips, velum (soft palate) and nasal cavities,
act as resonant cavities. These cavities modify the excitation spectrum that is emitted as vibrating
sounds. Vowel sounds are produces with an open vocal tract with very little audible obstruction
restricting the movement of air. Consonant sounds are produced with a relatively closed vocal
tract, from temporary closure or narrowing of air passageway, resulting in high audible effect on
the flow of air.
4
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Fig1: Human generation of speech
A very basic model of speech production can be determined by approximating the individual
processes of an excitation source, an acoustic filter (the vocal tract response) and the mouth
characteristics during speech [1].
Fig2: basic model of speech production
Speech Signal
Time Domain Representation
Digital signal analysis of speech waves separates the speech into voiced (contains harmonic
structure) and unvoiced speech (no harmonics structure, resembles white noise). For voiced
speech, the opening and closing of the glottis results in a series of glottal pulses. This excitation
possesses a periodic behavior, where each glottal opening-and-closing cycle varies in shape and
time period. A string of consecutive glottal pulses, also referred to as pitch pulses, results in a
quasi-periodic excitation waveform [2]
An example of speech containing the word [she] can be seen in figure below. Unvoiced segments
[sh] do not display any periodic behavior, whereas, the voiced segments [e] contain an obvious
periodic behavior in time domain [2].
5
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