Advanced Digital Signal Processing and Noise Reduction

by Vaseghi, Saeed V.

• ISBN13: 9780470740156
• ISBN10: 0470740159
• Edition: 4th
• Format: eBook
• Copyright: 2009-03-18
• Publisher: Wiley

Summary

Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system.The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods.Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysisComprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systemsExamples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction modelsAdvanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.

Table of Contents

ContentsSymbolsAbbreviations1 Introduction1.1 Signals, Noise and Information1.2 Signal Processing Methods1.3 Applications of Digital Signal Processing1.4 A Review of Sampling and Quantisation1.5 SummaryBibliography2 Noise and Distortion2.1 Introduction2.2 White Noise2.3 Coloured Noise; Pink Noise and Brown Noise2.4 Impulsive and Click Noise2.5 Impulsive and Click Noise2.6 Thermal Noise2.7 Shot Noise2.8 Flicker (I/f) Noise2.9 Burst Noise2.10 Electromagnetic (Radio) Noise2.11 Channel Distortions2.12 Echo and Multi-path Reflections2.13 Modelling Noise2.14 SummaryBibliography3 Information Theory and Probability Models3.1 Introduction: Probability and Information Models3.2 Random Processes3.3 Probability Models3.4 Information Models3.5 Stationary and Non-stationary Processes3.6 Expected Values of a Process3.7 Some Useful Classes of Random Processes3.8 Transformation of a Random Process3.9 Search Engines: Citation Ranking3.10 SummaryBibliography4 Baseyian Inference4.1 Bayesian Estimation Theory: Basic Definitions4.2 Bayesian Estimation4.3 The Estimate-Maximise Method4.4 Cramer-Rao Bound on the Minimum Estimator Variance4.5 Design of Gaussian Mixture Models4.6 Bayesian Classification4.7 Modeling the Space of a Random Process4.8 SummaryBibliography5 Hidden Markov Models5.1 Statistical Models for Non-Stationary Processes5.2 Hidden Markov Models5.3 Training Hidden Markov Models5.4 Decoding of Signals Using Hidden Markov Models5.5 HMM In DNA and Protein Sequence Modelling5.6 HMMs for Modelling Speech and Noise5.7 SummaryBibliography6 Least Square Error Wiener-Kolmogorov Filters6.1 Least Square Error Estimation: Wiener-Kolmogorov Filter6.2 Block-Data Formulation of the Wiener Filter6.3 Interpretation of Wiener Filters as Projection in Vector Space6.4 Analysis of the Least Mean Square Error Signal6.5 Formulation of Wiener Filters in the Frequency Domain6.6 Some Applications of Wiener Filters6.7 Implementation of Wiener Filters6.8 SummaryBibliography7 Adaptive Filters, Kalman, RLS, LMS7.1 Introduction7.2 State-Space Kalman Filter7.3 Extended Kalman Filter7.4 Unscented Kalman Filter7.5 Sample-Adaptive Filters7.6 Recursive Least Square(RLS) Adaptive Filters7.7 The Steepest-Descent Method7.8 The LMS Filter7.9 SummaryBibliography8 Linear Prediction Models8.1 Linear Prediction Coding8.2 Forward, Backward and Lattice Predictors8.3 Short-term and Long-Term Linear Predictors8.4 MAP Estimation of Predictor Coefficients8.5 Formant-Tracking LP Models8.6 Sub-Band Linear Prediction8.7 .i.Signal Restoration Using Linear Prediction Models8.8 Summary#6