Stochastic signal processing plays a central role in telecommunications and is employed in a range of applications from speech technology, audio and radar signal processing to pattern analysis and data forecasting. This book provides a coherent and structured representation of the theory and practice of stochastic signal processing and its application to digital noise reduction and equalisation.

Features include a thorough introduction to the fundamentals of random processes and estimation theory; comprehensive coverage of the theory of stochastic signal processing, including Bayesian estimation and classification, least squared error filters, and hidden Markov models; key applied subjects in noise reduction, signal restoration, channel equalisation and parameter estimation in noisy environments; extensive and up-to-date referencing throughout; and useful self-assessment solved examples. The graphical and highly accessible approach will appeal to a broad range of readers, including professional engineers in the telecommunications, audio and signal processing industry. Senior undergraduate and post-graduate students will also find this an invaluable reference. In particular, it will be ideal to supplement advanced courses in digital signal processing and statistical data analysis, as well as MSc and PhD courses in telecommunications.

1 Introduction
2 Stochastic Processes
3 Bayesian Estimation and Classification
4 Hidden Markov Models
5 Wiener Filters
6 Kalman and Adaptive Least Squared Error Filters
7 Linear Prediction Models
8 Power Spectrum Estimation
9 Spectral Subtraction
10 Interpolation
11 Impulsive Noise
12 Transient Noise
13 Echo Cancellation
14 Blind Deconvolution and Channel Equalisation
Frequently used Symbols and Abbreviations
Index