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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
4.4.2 LSP decoder 72
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 LSP decoder 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.
REFERENCES
[1] A.M Kondoz, Digital Speech, John Wiley & sons, January 2001.
[2] David Flanagan, Java in a Nutshell, OReilly, August 1998.
[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.
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Error Criteria. IEEE Trans. On ASSP, 27(3):247-254, June 1979.
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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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301, Paris, France, September 1989.
[16] L. Rabiner and R. Schafer. Digital Processing of Speech Signals. Signal Processing.
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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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http://www.excelsior-usa.com/http://www.excelsior-usa.com/http://www.excelsior-usa.com/
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