低码率语音编解码技术解析

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"低码率语音编解码方法是关于语音编码技术的一本教材，由A.M.Kondoz撰写，他在英国萨里大学任职。这本书详细介绍了用于低比特率通信系统的数字语音编码技术，旨在帮助读者理解和掌握算法，尽管未包含实际代码实现。" 在语音通信领域，低码率语音编解码技术是一项关键的技术，它主要用于限制带宽资源有限的通信系统，如无线通信、卫星通信以及互联网语音服务（如VoIP）。这种技术的目标是在保持可接受的语音质量的同时，尽可能地压缩语音数据，从而节省传输带宽。在《Digital Speech: Coding for Low Bit Rate Communication Systems》第二版中，作者A.M.Kondoz深入探讨了多种低码率语音编码算法。这些算法通常包括以下几种类型： 1. 声码器（Vocoder）：声码器是一种模拟人类听觉系统的模型，通过分析语音信号的频谱特性，将其转换为更紧凑的数据形式。例如，线性预测编码（LPC）、自适应差分脉冲编码（ADPCM）和码激励线性预测（CELP）等。 2. 基于感知的编码：这类编码方法更关注人类听觉系统的感知特性，例如，人耳对不同频率的敏感度不同。通过这种方式，可以牺牲部分不易察觉的频率成分，以降低码率。比如，多频带激励线性预测（MB-LTP）、感知熵编码（Pulse Excited Linear Prediction, PELP）。 3. 波形编码：这种方法试图直接对原始语音波形进行压缩，但通常需要较高的码率。如脉冲编码调制（PCM）和差分脉冲编码调制（DPCM）。 4. 混合编码：结合了声码器和波形编码的特点，如混合码激励线性预测（Hybrid CELP）和增强型全速率（EFR）编码。 5. 嵌入式编码：如AMR（Adaptive Multi-Rate）和Opus等，它们能够根据网络条件动态调整编码质量，以适应变化的带宽需求。 6. 人工神经网络和深度学习的应用：近年来，神经网络在语音编码中扮演了重要角色，如深度神经网络激励线性预测（DNN-ILP）和自注意力模型等，能进一步提升编码效率和语音质量。这本书的详尽公式解析有助于读者理解这些复杂的算法原理，尽管缺乏实际代码实现，但它为理论学习和进一步研究提供了坚实的基础。对于那些想要深入理解低码率语音编码算法或进行相关研究的人来说，这是一本不可多得的参考资料。

xiv Preface

on highly degraded channels, raising the acute problem of maintaining

acceptable speech quality from sensitive speech parameters even in bad

channel conditions. Moreover, when estimating these parameters from

the input, speech contaminated by the environmental noise typical of

mobile/wireless communication systems can cause signiﬁcant degradation

of speech quality.

These problems are by no means insurmountable. The advent of faster and

more reliable Digital Signal Processor (DSP) chips has made possible the easy

real-time implementation of highly complex algorithms. Their sophistication

is also exploited in the implementation of more effective echo control, back-

ground noise suppression, equalization and forward error control systems.

The design of an optimum system is thus mainly a trading-off process of many

factors which affect the overall quality of service provided at a reasonable

cost.

This book presents some existing chapters from the ﬁrst edition, as well

as chapters on new speech processing and coding techniques. In order

to lay the foundation of speech coding technology, it reviews sampling,

quantizations and then the basic nature of speech signals, and the theory and

tools applied in speech coding. The rest of the material presented has been

drawn from recent postgraduate research and graduate teaching activities

within the Multimedia Communications Research Group of the Centre for

Communication Systems Research (CCSR), a teaching and research centre

at the University of Surrey. Most of the material thus represents state-of-

the-art thinking in this technology. It is suitable for both graduate and

postgraduate teaching. For lecturing purposes, electronic versions of the

ﬁgures are available at ftp://ftp.wiley.co.uk/pub/books/kondoz. It is hoped

that the book will also be useful to research and development engineers for

whom the hands-on approach to the base band design of low bit-rate ﬁxed

and mobile communication systems will prove attractive.

Ahmet Kondoz

Introduction

Although data links are increasing in bandwidth and are becoming faster,

speech communication is still the most dominant and common service in

telecommunication networks. The fact that commercial and private usage of

telephony in its various forms (especially wireless) continues to grow even

a century after its ﬁrst inception is obvious proof of its popularity as a form

of communication. This popularity is expected to remain steady for the fore-

seeable future. The traditional plain analogue system has served telephony

systems remarkably well considering its technological simplicity. However,

modern information technology requirements have introduced the need for

a more robust and ﬂexible alternative to the analogue systems. Although the

encoding of speech other than straight conversion to an analogue signal has

been studied and employed for decades, it is only in the last 20 to 30 years

that it has really taken on signiﬁcant prominence. This is a direct result of

many factors, including the introduction of many new application areas.

The attractions of digitally-encoded speech are obvious. As speech is con-

densed to a binary sequence, all of the advantages offered by digital systems

are available for exploitation. These include the ease of regeneration and

signalling, ﬂexibility, security, and integration into the evolving new wire-

less systems. Although digitally-encoded speech possesses many advantages

over its analogue counterpart, it nevertheless requires extra bandwidth for

transmission if it is directly applied (without compression). The 64 kb/s

Log-PCM and 32 kb/s ADPCM systems which have served the many early

generations of digital systems well over the years have therefore been found

to be inadequate in terms of spectrum efﬁciency when applied to the new,

bandwidth limited, communication systems, e.g. satellite communications,

digital mobile radio systems, and private networks. In these and other sys-

tems, the bandwidth and power available is severely restricted, hence signal

compression is vital. For digitized speech, the signal compression is achieved

via elaborate digital signal processing techniques that are facilitated by the

Digital Speech. A. Kondoz

 2004 John Wiley & Sons, Ltd ISBN 0-470-87007-9 (HB)

2 Introduction

rapid improvement in digital hardware which has enabled the use of sophis-

ticated digital signal processing techniques that were not feasible before. In

response to the requirement for speech compression, feverish research activ-

ity has been pursued in all of the main research centres and, as a result, many

different strategies have been developed for suitably compressing speech for

bandwidth-restricted applications. During the last two decades, these efforts

have begun to bear fruit. The use of low bit-rate speech coders has been

standardized in many international, continental and national communication

systems. In addition, there are a number of private network operators who

use low bit-rate speech coders for speciﬁc applications.

The speech coding technology has gone through a number of phases starting

with the development and deployment of PCM and ADPCM systems. This

was followed by the development of good quality medium to low bit-rate

coders covering the range from 16 kb/s to 8 kb/s. At the same time, very

low bit-rate coders operating at around 2.4 kb/s produced better quality

synthetic speech at the expense of higher complexity. The latest trend in

speech coding is targeting the range from about 6 kb/s down to 2 kb/s by

using speech-speciﬁc coders, which rely heavily on the extraction of speech-

speciﬁc information from the input source. However, as the main applications

of the low to very low bit-rate coders are in the area of mobile communication

systems, where there may be signiﬁcant levels of background noise, the

accurate determination of the speech parameters becomes more difﬁcult.

Therefore the use of active noise suppression as a preprocessor to low bit-rate

speech coding is becoming popular.

In addition to the required low bit-rate for spectral efﬁciency, the cost

and power requirements of speech encoder/decoder hardware are very

important. In wireless personal communication systems, where hand-held

telephones are used, the battery consumption, cost and size of the portable

equipment have to be reasonable in order to make the product widely

acceptable.

In this book an attempt is made to cover many important aspects of low bit-

rate speech coding. In Chapter 2, the background to speech coding, including

the existing standards, is discussed. In Chapter 3, after brieﬂy reviewing the

sampling theorem, scalar and vector quantization schemes are discussed and

formulated. In addition, various quantization types which are used in the

remainder of this book are described.

In Chapter 4, speech analysis and modelling tools are described. After

discussing the effects of windowing on the short-time Fourier transform

of speech, extensive treatment of short-term linear prediction of speech is

given. This is then followed by long-term prediction of speech. Finally,

pitch detection methods, which are very important in speech vocoders, are

discussed.

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