By Saeed V. Vaseghi
Chapter 1 creation (pages 1–28):
Chapter 2 Noise and Distortion (pages 29–43):
Chapter three likelihood versions (pages 44–88):
Chapter four Bayesian Estimation (pages 89–142):
Chapter five Hidden Markov types (pages 143–177):
Chapter 6 Wiener Filters (pages 178–204):
Chapter 7 Adaptive Filters (pages 205–226):
Chapter eight Linear Prediction versions (pages 227–262):
Chapter nine energy 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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Extra resources for Advanced Digital Signal Processing and Noise Reduction, Second Edition
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.
3 Bayesian Statistical Signal Processing The fluctuations of a purely random signal, or the distribution of a class of random signals in the signal space, cannot be modelled by a predictive equation, but can be described in terms of the statistical average values, and modelled by a probability distribution function in a multidimensional signal space. For example, as described in Chapter 8, a linear prediction model driven by a random signal can model the acoustic realisation of a spoken word. However, the random input signal of the linear prediction model, or the variations in the characteristics of different acoustic realisations of the same word across the speaking population, can only be described in statistical terms and in terms of probability functions.
IEEE, 63, pp. 1692-1716. WIENER N. (1948) Extrapolation, Interpolation and Smoothing of Stationary Time Series. MIT Press, Cambridge, MA. WIENER N. (1949) Cybernetics. MIT Press, Cambridge, MA. A. A. (1963) Linear System Theory: The StateSpace Approach. McGraw-Hill, NewYork. Advanced Digital Signal Processing and Noise Reduction, Second Edition. Saeed V. 10 Thermal Noise Shot Noise Electromagnetic Noise Channel Distortions Modelling Noise oise can be defined as an unwanted signal that interferes with the communication or measurement of another signal.