Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distortion is an important part of a signal processing system.

Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system.

The 4th edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise,interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods.

Preface

Acknowledgements

Symbols

Abbreviations

1 Introduction

2 Noise and Distortion

3 Information Theory and Probability Models

4 Bayesian Inference

5 Hidden Markov Models

6 Least Square Error Wiener-Kolmogorov Filters

7 Adaptive Filters:Kalman,RLS,LMS

8 Linear Prediction Models

9 Eigenvalue Analysis and Principal Componet Analysis

10 Power Spectrum Analysis

11 Interpolation-Replacement of Lost Samples

12 Signal Enhancement via Spectral Amplitude Estimation

13 Impulsive Noise:Modelling,Detection and Removal

14 Transient Noise Pulses

15 Echo Cancellation

16 Channel Equalisation and Blind Deconvolution

17 Speech Enhancement:Noise Reduction,Bandwidth Extension and Packet Replacement

18 Multiple-Input Multiple-Output Systems,Independent Commponent Analysis

19 Signal Processing in Mobile Communication

Index