report format for final yr

8/4/2019 Report Format for Final Yr

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Study and Implementation of ITU-T G.723.1

Group Members

1. M.Sajjad.Khan 07-HITEC-EE-01

2. Kamran khan 07-HITEC-EE-02

3. Qaisar Khan 07-HITEC-EE-03

Project Advisor

(Dr.Jameel Ahmed)Professor

Head, Department of Electrical

Department of Electronic & Computer EngineeringNFC Institute of Engineering & Technological Training Multan-Pakistan

July 2006


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Department of Electronic & Computer EngineeringNFC Institute of Engineering & Technological Training, Multan-Pakistan

The project ___________________________________________, presented by:

1. M. Sajjad Khan 2K1-Electro-01

2. Kamran Khan 2K1-Electro-02

3. Qaisar Khan 2K1-Electro-03

under the supervision of their project advisor and approved by the project

examination committee, has been accepted by the NFC Institute of Engineering &

Technological Training, in partial fulfillment of the requirements for the four year

degree of B.Sc ( Electronic Engineering).

__________________ _______________

(Engr. Abdul Manan) (Dr. M. Ali Unar)Lecturer Professor

Internal Examiner External Examiner

__________________(Engr. Jameel Ahmed)

Associate Professor Head, Department of Electronic

& Computer Engineering


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DEDICATION

In this page you can dedicate your project to which you want

to dedicate your work.


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ACKNOWLEDGMENT

Acknowledgement is due to NFC Institute of Engineering & Technological Training

for support of this Project.

In this page you are advised to give appreciation to those teachers who have

helped you during your projects, and also the name of those who have guide you

through out your project thesis, evaluation.


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TABLE OF CONTENTS

Certificate ii

Dedication iii

Acknowledgement iv

Table of Contents v

List of Tables x

List of Figures xi

Abstract xiii

CHAPTER 1: INTRODUCTION 1

CHAPTER 2: LITERATURE SURVEY

5

2

CHAPTER 3: SPEECH CODING TECHNIQUES 9

3.1 Basic properties of speech coding 9

3.2 Classes of speech 12

3.2.1 Voiced sounds 123.2.2 Unvoiced sounds 12

3.2.3 Plosive sounds 13

Properties of speech 16

Speech modeling 16

Waveform coding 17

3.5.1 Pulse code modulation 19

3.5.2 Delta modulation 19

3.5.3 Adaptive differential PCM 19

3.6 Vocoding 20

3.6.1 Types of vocoders 22

3.6.1.1 Homomorphic vocoders 22

3.6.1.2 Linear predictive vocoders 23

3.7 Hybrid coding 24

3.7.1 Regular pulse excited coding 25

3.7.2 Code excited linear predictor coders 26

3.8 The G.728 Recommendation 27

3.9 The G.729 Recommendation 27

3.10 The G.723.1 Recommendation 28


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CHAPTER 4: UNDERSTANDING THE G.723.1 STANDARD 29

4.1 Introduction 29

4.1.1 Scope 30

4.1.2 Bit rates 304.1.3 Possible input signals 30

4.1.4 Delay 30

4.2 Speech coder description 31

4.2.1 Analysis-by-Synthesis coding techniques 31

4.3 Encoder principle 40

4.3.1 Framer 42

4.3.2 High Pass Filter 44

4.3.3 LPC analysis 44

4.3.3.1 Autocorrelation method 48

4.3.4 LSP quantizer 48

4.3.4.1 LPC to LSP conversion 514.3.4.2 Quantization of the LPC coefficients 51

4.3.5 LSP decoder 52

4.3.6 LSP interpolation 53

4.3.7 Formant Perceptual weighting filter 53

4.3.8 Pitch estimation 56

4.3.9 Harmonic noise shaping 56

4.3.10 Impulse response calculator 57

4.3.11 Zero input response and ringing subtraction 58

4.3.12 Pitch prediction 58

4.3.12.1 Adaptive codebook 58

4.3.13 Multi pulse LPC 59

4.3.14 High rate excitation (MP-MLQ) 61

4.3.15 Code excited linear predictive coding (CELP) 62

4.3.16 Low rate excitation (ACELP) 64

4.3.17 Excitation decoder 67

4.3.18 Decoding of the pitch information 68

4.3.19 Memory update 69

4.4 Decoder Principle 69

4.4.1 General description 70


4.4.3 LSP interpolator 724.4.4 Decoding of the pitch information 72

4.4.5 Excitation Decoder 72

4.4.6 Pitch postfilter 72

4.4.7 LPC synthesis filter 74

4.4.8 Formant postfilter 75

4.4.9 Gain scaling unit 75


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CHAPTER 5: SYSTEM DESIGN 77

5.1 Introduction 77

5.2 Use cases 77

5.3 Detailed use cases 805.3.1 Encoder 80

5.3.1.1 Set Rate 80

5.3.1.2 Allocate Buffer 80

5.3.1.3 Analyze LPC 80

5.3.1.4 Quantize LSP 80

5.3.1.5 Weighting Formants 82

5.3.1.6 Estimate Pitch 82

5.3.1.7 Shape Noise 82

5.3.1.8 Predict Pitch 82

5.3.1.9 Encode Excitation 82

Decoder 835.3.2.2 Allocate Buffer 83

5.3.2.3 Decode LSP 83

5.3.2.4 Interpolate LSP 83

5.3.2.5 Decode Pitch 83

5.3.2.6 Decode Excitation 83

5.3.2.7 Postfilter Pitch 85

5.3.2.8 Synthesize Signal 85

5.3.2.9 Postfilter Formants 85

Collaboration Diagrams 85

Collaboration Diagrams of the Encoder 85

Collaboration Diagrams of the Decoder 95

Class Diagram 104

Classes and attributes 104

Class associations 104

Methods and attributes type information 105

CHAPTER 6:IMPLEMENTATION OF THE G.723.1 STANDARD 108

6.1 Introduction 108

6.2 Speech coding considerations 108

6.2.1 Platform independency 109

6.2.2 Robustness 1096.2.3 Security 110

6.2.4 Object orientation facility 110

6.3 Java compared 111

6.4 Algorithmic description 114

6.4.1 Framer 114

6.4.2 High pass filter 114

6.4.2.1 Rem_Dc 115


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6.4.3 LPC analysis 115

6.4.3.1 Comp_Lpc 115

6.4.3.2 Durbin 116

6.4.4 LSP quantizer 117

6.4.4.1 AtoLsp 117

6.4.4.2 LspQnt 1186.4.4.3 Lsp_Svq 119


6.4.5.1 Lsp_Inq 119

6.4.6 LSP interpolation 120

6.4.6.1 Lsp_Int 121

6.4.6.2 LsptoA 121

6.4.7 Format perceptual weighting filter 122

6.4.7.1 Wght_Lpc 122

6.4.7.2 Error_Wght 122

6.4.8 Pitch estimation 123

6.4.8.1 Estim_Pitch 1236.4.9 Harmonic noise shaping filter 124

6.4.9.1 Comp_Pw 124

6.4.9.2 Filt_Pw 124

6.4.10 Impulse response calculator 125

6.4.10.1 Comp_Ir 125

6.4.11 Zero input response and ringing subtraction 126

6.4.11.1 Sub_Ring 126

6.4.12 Pitch prediction 126

6.4.12.1 Find_Acbk 127

6.4.12.2 Get_Rez 128

6.4.12.3 Decod_Acbk 128

6.4.13 High rate excitation (MP-MLQ) 129

6.4.13.1 Find_Fcbk 129

6.4.13.2 Find_Best 130

6.4.13.2 Find_Best 130

6.4.13.3 Gen_Trn 131

6.4.13.4 Fcbk_Pack 131

6.4.14 Low rate excitation (ACELP) 132

6.4.14.1 search_T0 132

6.4.14.2 ACELP_LBC_code 132

6.4.14.3 Cor_h 1326.4.14.4 Cor_h_X 132

6.4.14.5 G_code 133

6.4.15 Excitation decoder 133

6.4.15.1 Fcbk_Unpk 133

6.4.16 Decoding of the pitch information 134

6.4.17 Memory update 134

6.4.17.1 Upd_Ring 134


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6.4.18 Bit allocation 135

6.4.18.1 Line_Pack 135

6.4.19 Pitch postfilter 141

6.4.19.1 Comp_Lpf 141

6.4.19.2 Find_B 142

6.4.19.3 Find_F 1426.4.19.4 Get_Ind 143

6.4.19.5 Filt_Lpf 143

6.4.20 LPC synthesis filter 144

6.4.20.1 Synt 144

6.4.21 Formant postfilter 145

6.4.21.1 Spf 145

6.4.21.2 Comp_En 146

6.4.22 Gain scaling unit 146

6.4.22.1 Scale 146

6.4.23 Frame interpolation handling 147

6.4.23.1 Comp_Info 1476.4.23.2 Regen 148

CHAPTER 7: RESULTS AND OBSERVATIONS 149

CHAPTER 8: CONCLUSION 166

APPENDIX A: TABULAR DISTRIBUTION FOR CODEC 168

APPENDIX B: CD CONTENTS 170

NOMENCLATURE 171

REFERENCES 173


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LIST OF TABLES

Table 4.1 ACELP excitation codebook 65

Table 6.1 Bit allocation of the 6.3 kbps coding algorithm 139

Table 6.2 Bit allocation of the 5.3 kbps coding algorithm 140

Table 7.1 Size in bytes of test vectors for encoder and decoder modes 151


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LIST OF FIGURES

Figure 3.1 Physical model of speech production 11

Figure 3.2 Periodic nature of a voiced sound 14

Figure 3.3 Attenuation of the power of a voiced sound 15

Figure 3.4 General speech production model of vocoders 21

Figure 4.1 Basic structure of AbS-LPC speech encoder 33

Figure 4.2 Frequency domain plot of the weighting filter of LPC envelop 36

Figure 4.3 Block diagram of the speech coder 41

Figure 4.4 Logical division of the frame 43

Figure 4.5 Source filter model of speech production 45

Figure 4.6 The zeros of the coefficients of the polynomial 50

Figure 4.7 Spectral envelop of output quantization noise 56

Figure 4.8 A typical pulse position structure for MPLPC 60

Figure 4.9 Block diagram of the speech decoder 71

Figure 5.1 High level use case diagram 79

Figure 5.2 Detailed use case diagram of the encoding process 81

Figure 5.3 Detailed use case diagram of the decoding process 84

Figure 5.5 Collaboration diagram of Analyze LPC 87

Figure 5.6 Collaboration diagram of Quantize LSP 88

Figure 5.7 Collaboration diagram of Weighting Formants 89

Figure 5.8 Collaboration diagram of Estimate Pitch 90Figure 5.9 Collaboration diagram of Shape Noise 91

Figure 5.10 Collaboration diagram of Predict Pitch 92

Figure 5.11 Collaboration diagram of Encode Excitation at 6.3 kbps 93

Figure 5.12 Collaboration diagram of Encode Excitation at 5.3 kbps 94

Figure 5.13 Collaboration diagram of decoders Allocate Buffer 96

Figure 5.14 Collaboration diagram of Decode LSP 97

Figure 5.15 Collaboration diagram of Interpolate LSP 98

Figure 5.16 Collaboration diagram of Decode Pitch 99

Figure 5.17 Collaboration diagram of Decode Excitation 100

Figure 5.18 Collaboration diagram of Postfilter Pitch 101

Figure 5.19 Collaboration diagram of Synthesize Signal 102Figure 5.20 Collaboration diagram of Postfilter Formants 103

Figure 5.21 Class diagram of the Codec 107

Figure 6.1 Programming languages compared 112

Figure 7.1 Delay comparison of test vector Overc53h.tin on Windows

Platform

152

Figure 7.2 Delay comparison of test vector Overc63h.tin on Windows

Platform

153


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Figure 7.3 Delay comparison of test vector Overd53.tco on Windows Platform 154

Figure 7.4 Delay comparison of test vector Overd63p.tco on Windows

Platform

155

Figure 7.5 Delay of test vector Overc53h.tin on Linux Platform 156

Figure 7.6 Delay of test vector Overc63h.tin on Linux Platform 157

Figure 7.7 Delay of test vector Overd53.tco on Linux Platform 158Figure 7.8 Delay of test vector Overd63p.tin on Linux Platform 159

Figure 7.9 Test of Overc53h.tin on the optimized codec 160

Figure 7.10 Test of Overc63h.tin on the optimized codec 161

Figure 7.11 Test of Overd53.tco on the optimized codec 162

Figure 7.12 Test of Overd63p.tco on the optimized codec 163

Figure 7.13 Distribution of computational load at High-rate 164

Figure 7.14 Distribution of computational load at Low-rate 165

ABSTRACT

Digital transmission of coded speech is becoming increasingly important in a

plethora of VoIP applications e.g. teleconferencing, video-on-demand, Internet

telephony etc, reaching a variety of platforms, which urges for secure, robust,

flexible and platform independent software. Traditionally the software to support

multimedia applications, were not accoutered with these requirements. In this

project, we describe the ITU G.723.1 dual rate speech coding algorithm, its


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implementation in Java, which results in the bit-exact, fixed-point mathematical

operations as specified by the recommendation. The results were tested for bit-by-

bit compatibility with the ITU-T standard using the test vectors provided by ITU.

The performance results from these tests carried on different platforms show the

versatility of our codec.

Chapter 1


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INTRODUCTION

Although with the emergence of optical fibers bandwidth in wired communications has

become inexpensive, there is a growing need for bandwidth conservation and

enhanced privacy in wireless cellular and satellite communications. In particular,

cellular communications have been enjoying a tremendous worldwide growth and

there is a great deal of R&D activity geared towards establishing global portable

communications through wireless personal communication networks (PCNs). On the

other hand, there is a trend toward integrating voice-related applications (e.g.,

voicemail) on desktop and portable personal computers - often in the context of

multimedia communications. Most of these applications require that the speech signal

is in digital format so that it can be processed, stored, or transmitted under software

control. Speech is generally band limited to 4 kHz (or 3.2 kHz) and sampled at 8kHz,

although digital speech brings flexibility and opportunities for encryption, it is also

associated (when uncompressed) with a high data rate and hence high requirements of

transmission bandwidth and storage. Speech Coding or Speech Compression is the

field concerned with obtaining compact digital representations of voice signals for the

purpose of efficient transmission or storage. Speech coding involves sampling and

amplitude quantization. While the sampling is almost invariably done at a rate equal toor greater than twice the bandwidth of analog speech, there has been a great deal of

variability among the proposed methods in the representation of the sampled

waveform. The objective in speech coding is to represent speech with a minimum

number of bits while maintaining its perceptual quality. The quantization or binary

representation can be direct or parametric. Direct quantization implies binary

representation of the speech samples themselves while parametric quantization

involves binary representation of speech model and/or spectral parameters.

The simplest non-parametric coding technique is Pulse Code Modulation (PCM), which is

simply a quantizer of sampled amplitudes. Speech coded at 64 kilobits per second (kbps)

using logarithmic PCM is considered as "non-compressed" and is often used as a reference

for comparisons. The term medium-rate for coding in the range of 8-16 kbps, low-rate for


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systems working below 8 kbps and down to 2.4 kbps, and very-low-rate for coders

operating below 2.4 kbps.

Speech coding at medium-rates and below is achieved using an analysis-synthesisprocess.

In the analysis stage, speech is represented by a compact set of parameters, which are

encoded efficiently. In the synthesis stage, these parameters are decoded and used in

conjunction with a reconstruction mechanism to form speech. Analysis can be open-loop

orclosed-loop.

In closed-loop analysis, the parameters are extracted and encoded by minimizing explicitly

a measure (usually the mean square) of the difference between the original and the

reconstructed speech. Therefore closed-loop analysis incorporates synthesis and hence this

process is also called analysis-by-synthesis. Parametric representations can be speech or

non-speech specific. Non-speech specific coders or waveform coders are concerned with

the faithful reconstruction of the time-domain waveform and generally operate at medium-

rates. Speech specific coders or voice coders (vocoders) rely on speech models and are

focused upon producing perceptually intelligible speech without necessarily matching the

waveform. Vocoders are capable of operating at very-low rates but also tend to produce

speech of synthetic quality.

Although this is the generally accepted classification in speech coding, there are coders

that combine features from both categories. For example hybrid coders, which rely on

analysis-by-synthesis linear prediction. Hybrid coders combine the coding efficiency of

vocoders with the high-quality potential of waveform coders by modeling the spectral

properties of speech (much like vocoders) and exploiting the perceptual properties of the

ear, while at the same time providing for waveform matching (much like waveform

coders). Modern hybrid coders can achieve communications quality speech at 8 kbits/s and

below at the expense of increased complexity.

The International Telecommunications Union (ITU) is an international standards

organization chartered by the United Nations to formulate worldwide communications


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standards. The members represent nearly every nation in the world, which delegates

typically from the largest telecommunication service providers and equipment

manufacturers in those member countries. All equipment manufactured is according to

these standards and this ensures compatibility of equipment and protocols worldwide. The

most widely adopted ITU standards for speech coding in multimedia applications, are

G.728, G.729 and G.723.1.

The speech compression technology, to be designated as G.723.1, has enabled visual

telephony over the public telephone network, among a variety of other teleconferencing

and multimedia applications. This technology operates at data rates as low as 6.3 and 5.3

kbps producing a substantial improvement in compression ratios over existing ITU

standards - while maintaining high speech quality. The high bit rate has a great quality.The low bit rate gives a good quality and provides system designers with additional

flexibility. The high quality speech is possible because of significant advances in the

digital speech compression introduced by the parties and by advances in digital signal

processing technologies.

The algorithm used for coding of speech at higher rate (6.3 kbps) is Multipulse Maximum

Likelihood Quantization (MP-MLQ) and for lower rate (5.3 kbps) is Algebraic-Code-

Excited Linear Prediction (ACELP). It is possible to switch between the two rates at any

frame boundary.

In this project we have studied and implemented the ITU G.723.1 speech codec in Java,

which provides in more flexible, extensible, robust, secure and platform independent

implementation.

The report is distributed in the following manner.

Chapter 2 presents the literature survey, which includes the overview from differentpublications on speech compression. In Chapter 3, we have examined characteristics of

human speech, which will serve as a foundation for discussing how voice can be analyzed

and synthesized. By discussing different voice-digitization methods, we will also coverdifferent international methods, laying the foundation for information presented in the

chapters followed. Chapter 4 presents a block-by-block explanation of the ITU G.723.1

dual rate speech coder. Chapter 5 illustrates the system design aspects of our codec.Chapter 6 deals with the implementation aspects and the software specifications of G.723.1


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in Java. Chapter 7 illustrates the observation made by executing our codec on different

machines and platforms. Chapter 8 extracts the conclusion of the research and offers

suggestions for future attempts in this area.

Chapter 2

LITERATURE SURVEY


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Andreas S. Spanias [5] provides an overview of speech coding methodologies with

emphasis on those algorithms that are part of the recent low-rate standards for cellular

communications. Although the emphasis is on the new low-rate coders, attempts to providea comprehensive survey by covering some of the traditional methodologies as well. Which

will not only point out key references but will also provide valuable background to the

beginners.

Richard V. Cox and Peter Kroon [19] have compared different ITU standards, which are

applicable to low bit-rate multimedia communications. ITU Rec.G.729 8 kb/s CS-ACELP

has a 15 ms algorithmic codec delay and provides network-quality speech. It was originally

designed for wireless applications, but is applicable for multimedia communications as

well. Annex A of Rec. G.729 is a reduced complexity version of the CS-ACELP coder. It

was designed explicitly for simultaneous voice and data applications that are prevalent in

low bit-rate multimedia communications. These two coders use the same bit-stream format

and can interoperate. ITU Rec. G.723.1 6.3 and 5.3 kb/s speech coder for multimedia

communications was designed originally for low bit-rate videophones. Its frame size of 30

ms and one-way algorithmic codec delay of 37.5 ms allow for a further reduction in bit rate

compared to the G.729 coder. In applications where low-delay is important, the delay of

G.723.1 may be too large. However, if the delay is acceptable, G.723.1 provides a lower

complexity alternative to G.729 at the expense of a slight degradation in quality. The

authors describe the attributes of speech coders such as bit rate, complexity, delay and

quality, and discuss the basic concepts of the three ITU coders by comparing their specific

attributes.

Kashif Israr Siddiqui et al. [21] gives a brief account of their work i.e. to implement and

optimize a dual-rate speech codec for real-time operation on TriMedia's Very Long

Instruction Word (VLIW) Digital Signal Processor (DSP), Central Processing Unit (CPU)

so that the speech codec can operate under limited processor resources. They implemented

the speech codec which has two-bit rates associated with it, 5.3 and 6.3 kbits/s. This codec


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was optimized to represent speech with a high quality at the above rates using a limited

amount of complexity.

Fu-Kun Chen et al. [24] have proposed condensed stochastic codebook search approaches

that progressively reduce the computation required for the algebraic code excited linear

predictive (ACELP) and multi-pulse maximum likelihood quantization (MP-MLQ) coders.

By reducing the candidates of the codebook before search procedure, the proposed

methods can effectively diminish the computation required for the ITU-T G.723.1 dual rate

speech coder. Their simulation results show that the proposed methods can save over 50

percent for the stochastic codebook search with perceptually intangible degradation in

speech quality.

J. P. Woodard and L. Hanzo [25], have considered extensions to the Analysis-by-Synthesis

(AbS) loop used in Code Excited Linear Predictive (CELP) speech codecs. They have

examined the methods for updating the short-term synthesis filter once the excitation

parameters have been determined. They show that significant improvements can be

achieved by updating the synthesis filter, similar to those obtained using the well-known

methods of interpolation and bandwidth expansion. However their proposed method of

update avoids the increase in the delay of a codec that is usually associated with

interpolation. Furthermore the traditional sequential method of determining the adaptive

and fixed codebook parameters is examined and compared to an exhaustive search of both

codebooks. Three sub-optimum techniques were proposed for improving the performance

of the codebook search while maintaining a reasonable level of complexity. The most

complex of these increases the codec complexity by only about 40% but provides 80% of

the maximum possible 1.1 dB segmental SNR improvement associated with an exhaustive

codebook search.

Benjamin W. Wah and Dong Lin [26]discuss a fundamental issue in real-time interactive

voice transmissions over unreliable IP networks due to the loss or late arrival of packets for

playback. This problem is especially serious when transmitting low bit rate-coded speech

with pervasive dependencies introduced. In such a case, the loss or late arrival of a single


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packet will lead to the loss of subsequent dependent frames. In their paper, they have

described end-to-end loss-concealment schemes for ensuring high quality in playback.

They propose a novel multiple description-coding methods for concealing packet losses in

transmitting low bit rate-coded speech. Based on high correlations observed in linear

predictor parameters in the form of Line Spectral Paris (LSPs) of adjacent frames, they

generate multiple descriptions in senders by interleaving LSPs, and reconstruct lost LSPs

in receivers by linear interpolations. As excitation codewords have low correlations, they

further enlarge the segment size for excitation generation and replicate excitation

codewords in all descriptions in order to maintain the same transmission bandwidth.

J. P. Woodard and L. Hanzo [27] have developed a programmable 8-16 kbps low-delay

speech codec, which is compatible with the G.728 16 kbps ITU codec at its top rate and

exhibits similarly attractive trade-offs in terms of speech quality, delay and complexity in

the range of 8-16 kbps.

Thomas J. Dillon, Jr. [36] application report describes how the G.723.1 Dual-Rate Speech

Coder has been implemented on the Texas Instruments (TIE) TMS320C62x digital signal

processor (DSP). Beyond the use of the C62x intrinsic functions, the application report

includes specific changes required to allow this coder to operate in a real-time system with

other speech coders. Also reported is information on several optimization techniques used

to yield multiple channels running concurrently. Finally, the application report includes the

performance resulting from this implementation of the algorithm.

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[1] A.M Kondoz, Digital Speech, John Wiley & sons, January 2001.

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[3] Jonathan (Y) Stein, Digital Signal Processing, John Wiley & sons, January 2000.


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[4] Sophocles J. Orfanidis, Introduction to Signal Processing, Prentice Hall, 1996.

[5] Andreas S. Spanias, Speech Coding: A Tutorial Review, Proc. IEEE, vol.82, no. 10,

pp. 1541-1582, October 1994.

[6] C. G. Bell et al. Reduction of speech spectra by Analysis-by-synthesis techniques,

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Communications, pages 600-614, April 1982.

[8] R. W. Schafer and B. S. Atal, Code Excited Linear Prediction (CELP): High Quality

Speech at Low Bit Rates, Proc of ICASSP, pages 937-940, 1985.

[9] P. Kroon and E. Deprettere, A Class of Analysis-By-Synthesis Predictive Coders for

High Quality Speech Coding at Rates Between 4.8 and 16 Kbps, IEEE jour. On Selected

Areas in Communications, pages 353-363, February 1988.

[10] M. R. Schroeder and B. S. Atal. Predictive Coding of Speech Signals and Subjective

Error Criteria. IEEE Trans. On ASSP, 27(3):247-254, June 1979.

[11] M. R. Schroeder, B. S. Atal, and J. L. Hall. Optimizing Digital Speech Coders by

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America, 66(6):1647-1652, December 1979.

[12] Edward Chilton. Factors Affecting the Quality of Linear Predictive Coding of

Speech at Low Bit-rates, PhD thesis, University of Surrey. Guildford, Surrey, U.K.,

October 1990.

[13] K. Y. Lee. Analysis By Synthesis Linear Predictive Coding, PhD thesis, University

of Surrey, Guildford, Surrey, U.K., October 1990.

[14] S. Singhal and B. S. Atal, Improving the performance of multi-pulse LPC coders at

low bit rates, In Proc. Of ICASSP, pages 1.3.1-1.3.4, San Diego, 1984.

[15] R. Soheili, J. Horos, A. M. Kondoz, and B. G. Evans. New Innovations in Multi-

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[17] John R. Deller, John G. Proakis, and John H. L. Hansen. Discrete-Time

Processing of Speech Signals. Macmillan, 1993.

[18] Luis Miguel Teixeira de Jesus, Speech Coding and Synthesis Using


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Parametric Curvess, MS thesis, University of East Anglia., October 1997.

[19] Richard V. Cox and Peter Kroon. Low Bit-Rate Speech Coders for Multimedia

Communication, Murray Hill, NJ 07904, November 1996.

[20] Thomas E. Tremain, The Government Standard Linear Predictive Coding

Algorithm: LPC-10, Speech Technology Magazine, p.40-49, April 1982.

[21] Kashif Israr Siddiqui et al. Real-Time Implementation of ITU-Ts G.723.1 Dual Rate

Speech Coder for Multimedia Communications Transmitting at 5.3 and 6.3 kbits/s on

Trimedias TM-1000 VLIW DSP CPU, Proc. of INMIC, December 2001.

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, IEICE Trans. Inf. & Syst., Vol.E85-D, No.1 January 2002[25] J.P. Woodard and L. Hanzo, Improvements to the Analysis-by-Synthesis Loop in

CELP Codecs, Department of Electronics and Computer Science, University of

Southampton, June 1994.

[26] Benjamin W. Wah and Dong Lin,LSP-Based Multiple-Description Coding for Real-TimeLow Bit-Rate Voice Transmissions Department of Electrical and Computer

Engineering and the Coordinated Science Laboratory University of Illinois, Urbana

Urbana, IL 61801, USA

[27] J.P. Woodard and L. Hanzo , A G 728-compatible Programmable Rate 8-16 kbps

Low-Delay CELP Codec, Department of Electronics and Computer Science, University

of Southampton, June 1994.

[28] Simon Haykin, Analog and Digital Communications, John Wiley & sons, 1994.

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Prentice Hall, 1998.

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Processing Series, 1996.

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[32] Peter Kraniauskas, Transforms in Signals and Systems, Addison-Wesley, 1993.

[33] Leland B. Jackson, Signals, Systems, and Transforms, Addison-Wesley, 1991.

[34] Alan V. Oppenheim, Signals and Systems, Prentice Hall, 1983.

[35] Justin Zobel, Writing for Computer Science, Springer, 1998.


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[36] Thomas J. Dillon, Jr., G.723.1 Dual-Rate Speech Coder: Multichannel

TMS320C62x Implementation, Application Report, SPRA552B, February 2000.

[37] Cisco Systems official documentation, Waveform Coding Techniques, Cisco

Systems Inc, 2001.

[38] Excelsior, Excelsior JET 2.5,http://www.excelsior-usa.com , 2002.

[39] Craig Larman, Applying UML and Patterns, Prentice Hall, 2000.

[40] Martin Fowler, UML Distilled, Addison-Wesley, 2000.

[41] Bertrand Meyer, Object-Oriented Software Construction, Prentice Hall, 1997.

[42] Bruce Eckel, Thinking in Java, Prentice Hall, 2000.
http://www.excelsior-usa.com/http://www.excelsior-usa.com/http://www.excelsior-usa.com/

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