By Saeed V. Vaseghi
Chapter 1 creation (pages 1–28):
Chapter 2 Noise and Distortion (pages 29–43):
Chapter three chance versions (pages 44–88):
Chapter four Bayesian Estimation (pages 89–142):
Chapter five Hidden Markov versions (pages 143–177):
Chapter 6 Wiener Filters (pages 178–204):
Chapter 7 Adaptive Filters (pages 205–226):
Chapter eight Linear Prediction types (pages 227–262):
Chapter nine strength Spectrum and Correlation (pages 263–296):
Chapter 10 Interpolation (pages 297–332):
Chapter eleven Spectral Subtraction (pages 333–354):
Chapter 12 Impulsive Noise (pages 355–377):
Chapter thirteen brief Noise Pulses (pages 378–395):
Chapter 14 Echo Cancellation (pages 396–415):
Chapter 15 Channel Equalization and Blind Deconvolution (pages 416–466):
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Additional info for Advanced Digital Signal Processing and Noise Reduction, Second Edition
Neural networks are particularly useful in non-linear partitioning of a signal space, in feature extraction and pattern recognition, and in decision-making systems. In some hybrid pattern recognition systems neural networks are used to complement Bayesian inference methods. Since the main objective of this book is to provide a coherent presentation of the theory and applications of statistical signal processing, neural networks are not discussed in this book. 3 Applications of Digital Signal Processing In recent years, the development and commercial availability of increasingly powerful and affordable digital computers has been accompanied by the development of advanced digital signal processing algorithms for a wide variety of applications such as noise reduction, telecommunication, radar, sonar, video and audio signal processing, pattern recognition, geophysics explorations, data forecasting, and the processing of large databases for the identification extraction and organisation of unknown underlying structures and patterns.
Bayesian inference theory provides a generalised framework for statistical processing of random signals, and for formulating and solving estimation and decision-making problems. Chapter 4 describes the Bayesian inference methodology and the estimation of random processes observed in noise. 4 Neural Networks Neural networks are combinations of relatively simple non-linear adaptive processing units, arranged to have a structural resemblance to the transmission and processing of signals in biological neurons.
Noise and distortion are the main limiting factors in communication and measurement systems. Therefore the modelling and removal of the effects of noise and distortion have been at the core of the theory and practice of communications and signal processing. Noise reduction and distortion removal are important problems in applications such as cellular mobile communication, speech recognition, image processing, medical signal processing, radar, sonar, and in any application where the signals cannot be isolated from noise and distortion.