Advanced Signal Processing and Noise Reduction, 2nd Edition

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This book presents a broad range of theory and application of statistical signal processing. The emphasis is on digital noise reduction algorithms, particularly important in the field of mobile communication. Vaseghi covers a broad range of applications, including spectral estimation, channel equalization, speech coding over noisy channels, active noise control, echo cancellation, and more.

Editorial Reviews

From the Back Cover

Signal processing and noise reduction are at the core of telecommunications and information processing systems. With the increasing use of digital cellular mobile systems in a variety of adverse environments, noise reduction is becoming a particularly important aspect of communication system design. This second edition provides a thoroughly revised and expanded introduction to the fundamentals of random processes, Bayesian modelling, and noise reduction. The subject is covered in a graphical and mathematically accessible manner with the emphasis on Bayesian inference and its application to noise reduction.
* Offers a comprehensive insight into a broad range of theory and applications of advanced signal processing
* Presents new chapters and sections on definition and modelling of different types of noise and distortions, multi-band linear prediction models, state-dependent Wiener filters and HMM-based noise reduction
* Explores practical solutions to echo cancellation, impulsive and transient noise removal, broad-band noise removal, channel equalisation, HMM-based signal and noise decomposition.
* Discusses topics such as probability theory, Bayesian estimation and classification, hidden Markov models, adaptive filters, multi-band linear prediction, spectral estimation and impulsive and transient noise removal
For professional engineers in telecommunications and audio and signal processing industries this updated second edition will be a valuable resource. Researchers and postgraduates in the fields of digital signal processing, statistical data analysis and telecommunications will also benefit from this extensive reference.

About the Author

Saeed Vaseghi is currently a Professor of Communications and Signal Processing at Brunel University’s department of Electronics and Computer Engineering and is Group Leader for the Communications & Multimedia Signal Processing Group.

Previously, Saeed obtained a first in Electrical and Electronics Engineering from Newcastle University, and a PhD in Digital Signal Processing from Cambridge University. His Ph.D. in noisy signal restoration led to establishment of CEDAR, the world’s leading system for restoration of audio signals. Saeed also held a British Telecom lectureship at UEA Norwich, and a readership at Queen’s University of Belfast before his move to Brunel. –This text refers to an out of print or unavailable edition of this title.

9 of 9 people found the following review helpful
Format:Hardcover
This is a book about digital signal processing noise reduction techniques. The selection of techniques covered is very broad, much more extensive than most books I have seen in this field.Unfortunately, the book has some severe shortcomings. As another reviewer has mentioned, the treatment of each technique is too shallow to be useful, and the bibliography much less than helpful. A helpful bibliography for each section would refer to more extensive treatments that might be usable for design and implementation. The bibliography for each chapter is instead dated and nonspecific, consisting of a seemingly random collection of technical reports, papers, and books published over three or four decades.The book is filled with equations, which are frustrating to read. They appear to have been typeset using a word processor that did not properly space mathematical symbols. For example, function parameters (in parentheses) are often closer to the following factor than the function name. The equations are often hard to read. The equations dealing with continuous functions are generally straightforward to interpret, but those dealing with discrete-time functions are frequently written with indices in parentheses instead of as subscripts. As you are reading through the mathematics, you have to separate in your mind the functions (with parameters) from the vector elements (with indices). The typesetting occasionally renders greek symbols in a bold font, so sometimes on the same page you will have the same symbols in different equations bolded or not bolded.

Another problem with the mathematics comes about because of the extensiveness of the material. Different signal processing techniques have different mathematical histories, and therefore different naming conventions. The author generally uses the conventional mathematical notation for each technique, leading to jarring transitions from section to section.

All in all, I think this could be a very useful book, if it were more carefully written, typeset adequately, if the treatment of each technique were better motivated and complete enough to use, and if the bibliography provided useful references to specialized treatments of individual topics.

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