By Saeed V. Vaseghi

Content material:
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):

Show description

Read or Download Advanced Digital Signal Processing and Noise Reduction, Second Edition PDF

Best microprocessors & system design books

Apple I Replica Creation: Back to the Garage

The Apple ][ used to be fairly the pc designed from the floor up that may kick off own computing on a wide scale. however the Apple I took the most important step of all. a few extremely simple thoughts are very challenging to do the 1st time. This laptop informed the area that small desktops should not back include geeky entrance panels, yet relatively with human keyboards, able to variety on.

Surviving the SOC Revolution - A Guide to Platform-Based Design

From the stories: "This publication crystallizes what may well develop into a defining second within the electronics - the shift to platform-based layout. It offers the 1st complete guidebook in case you will construct, and use, the mixing systems which can quickly force the system-on-chip revolution. " digital Engineering instances

Language-driven Exploration and Implementation of Partially Re-configurable ASIPs

Expanding complexity of recent embedded platforms calls for process designers to ramp up their layout productiveness with no compromising functionality ambitions. this can be promoted by means of glossy digital method point (ESL) strategies. Language-driven Exploration and Implementation of partly Re-configurable ASIPs addresses an enormous phase of the ESL region by means of modeling in part re-configurable processors through high-level structure Description Language (ADL).

Object-Oriented Technology. ECOOP 2004 Workshop Reader: ECOOP 2004 Workshop, Oslo, Norway, June 14-18, 2004, Final Reports

This yr, for the 8th time, the eu convention on Object-Oriented Programming (ECOOP) sequence, in cooperation with Springer, is joyful to o? er the object-oriented learn group the ECOOP 2004 Workshop Reader, a compendium of workshop stories relating the ECOOP 2004 convention, held in Oslo from June 15 to 19, 2004.

Extra resources for Advanced Digital Signal Processing and Noise Reduction, Second Edition

Example text

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.

Download PDF sample

Rated 4.33 of 5 – based on 29 votes