Advanced Digital Signal Processing and Noise Reduction

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John Wiley & Sons, Dec 23, 2008 - Science - 544 pages
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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 fourth 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.

  • Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis

  • Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems

  • Examples and applications in signal and information extraction from noisy data

  • Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models

Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.

 

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Contents

1 Introduction
1
2 Noise and Distortion
35
3 Information Theory and Probability Models
51
4 Bayesian Inference
107
5 Hidden Markov Models
147
6 Least Square Error WienerKolmogorov Filters
173
7 Adaptive Filters Kalman RLS LMS
193
8 Linear Prediction Models
227
12 Signal Enhancement via Spectral Amplitude Estimation
321
13 Impulsive Noise Modelling Detection and Removal
341
14 Transient Noise Pulses
359
15 Echo Cancellation
371
16 Channel Equalisation and Blind Deconvolution
391
17 Speech Enhancement Noise Reduction Bandwidth Extension and Packet Replacement
423
18 MultipleInput MultipleOutput Systems Independent Component Analysis
467
19 Signal Processing in Mobile Communication
491

9 Eigenvalue Analysis and Principal Component Analysis
257
10 Power Spectrum Analysis
271
11 Interpolation Replacement of Lost Samples
295

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About the author (2008)

SAEED V. VASEGHI, Brunel University, UK

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