Tài liệu SIGNAL PROCESSING FOR TELECOMMUNICATIONS AND MULTIMEDIA MULTIMEDIA docx

SIGNAL PROCESSING FOR

TELECOMMUNICATIONS

AND MULTIMEDIA

MULTIMEDIA SYSTEMS AND

APPLICATIONS SERIES

Consulting Editor

Borko Furht

Florida Atlantic University

[email protected]

Recently Published Titles:

ADVANCED WIRED AND WIRELESS NETWORKS edited by Tadeusz A. Wysocki, Arek

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SIGNAL PROCESSING FOR

TELECOMMUNICATIONS

AND MULTIMEDIA

edited by

Tadeusz A. Wysocki

University of Wollongong, Australia

Bahram Honary

Lancaster University, UK

Beata J. Wysocki

University of Wollongong, Australia

Springer

eBook ISBN: 0-387-22928-0

Print ISBN: 0-387-22847-0

Print ©2005 Springer Science + Business Media, Inc.

All rights reserved

No part of this eBook may be reproduced or transmitted in any form or by any means, electronic,

mechanical, recording, or otherwise, without written consent from the Publisher

Created in the United States of America

Boston

©2005 Springer Science + Business Media, Inc.

Visit Springer's eBookstore at: http://ebooks.kluweronline.com

and the Springer Global Website Online at: http://www.springeronline.com

CONTENTS

PART I: MULTIMEDIA SOURCE PROCESSING

1. A Cepstrum Domain HMM-Based Speech Enhancement Method

Applied to Non-stationary Noise

2. Time Domain Blind Separation of Nonstationary Convolutively

Mixed Signals

3.

4. Objective Hybrid Image Quality Metric for In-Service Quality

Assessment

5. An Object-Based Highly Scalable Image Coding for Efficient

MultimediaDistribution

6. Classification of Video Sequences in MPEG Domain

M.Nilsson, M.Dahl, and I.Claesson

Preface ix

I.T.Russel, J.Xi, and A.Mertins 15

1

Speech and Audio Coding Using Temporal Masking

T.S.Gunavan, E.Ambikairajah, and D.Sen 31

T.M.Kusuma, and H.-J.Zepernick 43

H.Danyali, and A.Mertins 57

W.Gillespie, and T.Nguyen 71

vi

PART II: ERROR-CONTROL CODING, CHANNEL

ACCESS, AND DETECTION ALGORITHMS

7. Unequal Two-Fold Turbo Codes

8. Code-Aided ML Joint Delay Estimation and Frame Synchronization

H.Wymeersch, and M.Moeneclaey 97

9. Adaptive Blind Sequence Detection for Time Varying Channel

M.N.Patwary, P.Rapajic, and I.Oppermann 111

10. Optimum PSK Signal Mapping for Multi-Phase Binary-CDMA

Systems

Y.-J.Seo,and Y.-H.Lee 125

11. A Complex Quadraphase CCMA Approach for Mobile Networked

Systems

K. L. Brown, and M. Darnell 135

12. Spatial Characterization of Multiple Antenna Channels

T.S.Pollock, T.D.Abhayapala, and R.A.Kennedy 145

13. Increasing Performance of Symmetric Layered Space-Time Systems

P. Conder and T. Wysocki 159

14. New Complex Orthogonal Space-Time Block Codes of Order Eight

J.Seberry, L.C.Tran, Y.Wang, B.J.Wysocki, T.A.Wysocki, T.Xia, and

Y.Zhao 173

PART III: HARDWARE IMPLEMENTATION

15. Design of Antenna Array Using Dual Nested Complex

Approximation

M.Dahl, T. Tran, I. Claesson, and S.Nordebo .183

16. Low-Cost Circularly Polarized Radial Line Slot Array Antenna for

IEEE 802.11 B/G WLAN Applications

S.Zagriatski, and M. E. Bialkowski 197

C.Tanriover, and B.Honary 87

vii

17. Software Controlled Generator for Electromagnetic Compatibility

Evaluation

P.Gajewski, and J.Lopatka 211

18. Unified Retiming Operations on Multidimensional Multi-Rate

Digital Signal Processing Systems

D.Peng, H.Sharif, and S.Ci 221

19. Efficient Decision Feedback Equalisation of Nonlinear Volterra

Channels

S.Sirianunpiboon, and J.Tsimbinos 235

20. A Wideband FPGA-Based Digital DSSS Modem

K.Harman, A.Caldow, C.Potter, J.Arnold, and G.Parker 249

21. Antennas for 5-6 GHz Wireless Communication Systems

Y.Ge, K.P.Esselle, and T.S.Bird 269

Index 281

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PREFACE

The unprecedented growth in the range of multimedia services offered

these days by modern telecommunication systems has been made possible

only because of the advancements in signal processing technologies and

algorithms. In the area of telecommunications, application of signal

processing allows for new generations of systems to achieve performance

close to theoretical limits, while in the area of multimedia, signal processing

the underlying technology making possible realization of such applications

that not so long ago were considered just a science fiction or were not even

dreamed about. We all learnt to adopt those achievements very quickly, but

often the research enabling their introduction takes many years and a lot of

efforts. This book presents a group of invited contributions, some of which

have been based on the papers presented at the International Symposium

on DSP for Communication Systems held in Coolangatta on the Gold Coast,

Australia, in December 2003.

Part 1 of the book deals with applications of signal processing to

transform what we hear or see to the form that is most suitable for

transmission or storage for a future retrieval. The first three chapters in this

part are devoted to processing of speech and other audio signals. The next

two chapters consider image coding and compression, while the last chapter

of this part describes classification of video sequences in the MPEG domain.

Part 2 describes the use of signal processing for enhancing performance

of communication systems to enable the most reliable and efficient use of

those systems to support transmission of large volumes of data generated by

multimedia applications. The topics considered in this part range from error￾control coding through the advanced problems of the code division multiple

x

access (CDMA) to multiple-input multiple-output (MIMO) systems and

space-time coding.

The last part of the book contains seven chapters that present some

emerging system implementations utilizing signal processing to improve

system performance and allow for a cost reduction. The issues considered

range from antenna design and channel equalisation through multi-rate

digital signal processing to practical DSP implementation of a wideband

direct sequence spread spectrum modem.

The editors wish to thank the authors for their dedication and lot of efforts in

preparing their contributions, revising and submitting their chapters as well

as everyone else who participated in preparation of this book.

Tadeusz A. Wysocki

Bahram Honary

Beata J. Wysocki

PART 1:

MULTIMEDIA SOURCE PROCESSING

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Chapter 1

A CEPSTRUM DOMAIN HMM-BASED SPEECH

ENHANCEMENT METHOD APPLIED TO NON￾STATIONARY NOISE

Mikael Nilsson, Mattias Dahl and Ingvar Claesson

Blekinge Institute of Technology, School of Engineering, Department of Signal Processing,

372 25 Ronneby, Sweden

Abstract: This paper presents a Hidden Markov Model (HMM)-based speech

enhancement method, aiming at reducing non-stationary noise from speech

signals. The system is based on the assumption that the speech and the noise

are additive and uncorrelated. Cepstral features are used to extract statistical

information from both the speech and the noise. A-priori statistical

information is collected from long training sequences into ergodic hidden

Markov models. Given the ergodic models for the speech and the noise, a

compensated speech-noise model is created by means of parallel model

combination, using a log-normal approximation. During the compensation, the

mean of every mixture in the speech and noise model is stored. The stored

means are then used in the enhancement process to create the most likely

speech and noise power spectral distributions using the forward algorithm

combined with mixture probability. The distributions are used to generate a

Wiener filter for every observation. The paper includes a performance

evaluation of the speech enhancer for stationary as well as non-stationary

noise environment.

Key words: HMM, PMC, speech enhancement, log-normal

1. INTRODUCTION

Speech separation from noise, given a-priori information, can be viewed

as a subspace estimation problem. Some conventional speech enhancement

methods are spectral subtraction [1], Wiener filtering [2], blind signal

separation [3] and hidden Markov modelling [4].

Hidden Markov Model (HMM) based speech enhancement techniques

are related to the problem of performing speech recognition in noisy

2 Chapter 1

environments [5,6]. HMM based methods uses a-priori information about

both the speech and the noise [4]. Some papers propose HMM speech

enhancement techniques applied to stationary noise sources [4,7]. The

common factor for these problems is to the use of Parallel Model

Combination (PMC) to create a HMM from other HMMs. There are several

possibilities to accomplish PMC including Jacobian adaptation, fast PMC,

PCA-PMC, log-add approx-imation, log-normal approximation, numerical

integration and weighted PMC [5,6]. The features for HMM training can be

chosen in different manners. However, the cepstral features have dominated

the field of speech recognition and speech enhancement [8]. This is due to

the fact that the covariance matrix, which is a significant parameter in a

HMM, is close to diagonal for cepstral features of speech signals.

In general, the whole input-space, with the dimension determined by the

length of the feature vectors, contains the speech and noise subspaces. The

speech subspace should contain all possible sound vectors from all possible

speakers. This is of course not practical and the approximated subspace is

found by means of training samples from various speakers and by averaging

over similar speech vectors. In the same manner the noise subspace is

approximated from training samples. In non-stationary noise environments

the noise subspace complexity increases compared to a stationary subspace,

hence a larger noise HMM is needed. After reduction it is desired to obtain

only the speech subspace.

The method proposed in this paper is based on the log-normal

approximation by adjusting the mean vector and the covariance matrix.

Cepstral features are treated as observations and diagonal covariance

matrices are used for hidden Markov modeling of the speech and noise

source. The removal of the noise is performed by employing a time

dependent linear Wiener filter, continuously adapted such that the most

likely speech and noise vector is found from the a-priori information. Two

separate hidden Markov models are used to parameterize the speech and

noise sources. The algorithm is optimized for finding the speech component

in the noisy signal. The ability to reduce non-stationary noise sources is

investigated.

2. FEATURE EXTRACTION FROM SIGNALS

The signal of concern is a discrete time noisy speech signal x(n), found

from the corresponding correctly band limited and sampled continuous

signal. It is assumed that the noisy speech signal consists of speech and

additive noise