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.

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.

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