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Advanced Digital Signal Processing and Noise Reduction, 4th Edition

ISBN: 978-0-470-74016-3
544 pages
December 2008
Advanced Digital Signal Processing and Noise Reduction, 4th Edition (0470740167) cover image


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 analysis

  • Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems

  • Examples 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 models

Advanced 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.

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Table of Contents




1 Introduction

1.1 Signals, Noise and Information

1.2 Signal Processing Methods

1.3 Applications of Digital Signal Processing

1.4 A Review of Sampling and Quantisation

1.5 Summary



2 Noise and Distortion

2.1 Introduction

2.2 White Noise

2.3 Coloured Noise; Pink Noise and Brown Noise

2.4 Impulsive and Click Noise

2.5 Impulsive and Click Noise

2.6 Thermal Noise

2.7 Shot Noise

2.8 Flicker (I/f) Noise

2.9 Burst Noise

2.10 Electromagnetic (Radio) Noise

2.11 Channel Distortions

2.12 Echo and Multi-path Reflections

2.13 Modelling Noise

2.14 Summary



3 Information Theory and Probability Models

3.1 Introduction: Probability and Information Models

3.2 Random Processes

3.3 Probability Models

3.4 Information Models

3.5 Stationary and Non-stationary Processes

3.6 Expected Values of a Process

3.7 Some Useful Classes of Random Processes

3.8 Transformation of a Random Process

3.9 Search Engines: Citation Ranking

3.10 Summary



4 Baseyian Inference

4.1 Bayesian Estimation Theory: Basic Definitions

4.2 Bayesian Estimation

4.3 The Estimate-Maximise Method

4.4 Cramer–Rao Bound on the Minimum Estimator Variance

4.5 Design of Gaussian Mixture Models

4.6 Bayesian Classification

4.7 Modeling the Space of a Random Process

4.8 Summary



5 Hidden Markov Models

5.1 Statistical Models for Non-Stationary Processes

5.2 Hidden Markov Models

5.3 Training Hidden Markov Models

5.4 Decoding of Signals Using Hidden Markov Models

5.5 HMM In DNA and Protein Sequence Modelling

5.6 HMMs for Modelling Speech and Noise

5.7 Summary



6 Least Square Error Wiener-Kolmogorov Filters

6.1 Least Square Error Estimation: Wiener-Kolmogorov Filter

6.2 Block-Data Formulation of the Wiener Filter

6.3 Interpretation of Wiener Filters as Projection in Vector Space

6.4 Analysis of the Least Mean Square Error Signal

6.5 Formulation of Wiener Filters in the Frequency Domain

6.6 Some Applications of Wiener Filters

6.7 Implementation of Wiener Filters

6.8 Summary



7 Adaptive Filters, Kalman, RLS, LMS

7.1 Introduction

7.2 State-Space Kalman Filter

7.3 Extended Kalman Filter

7.4 Unscented Kalman Filter

7.5 Sample-Adaptive Filters

7.6 Recursive Least Square(RLS) Adaptive Filters

7.7 The Steepest-Descent Method

7.8 The LMS Filter

7.9 Summary



8 Linear Prediction Models

8.1 Linear Prediction Coding

8.2 Forward, Backward and Lattice Predictors

8.3 Short-term and Long-Term Linear Predictors

8.4 MAP Estimation of Predictor Coefficients

8.5 Formant-Tracking LP Models

8.6 Sub-Band Linear Prediction

8.7 .i.Signal Restoration Using Linear Prediction Models

8.8 Summary



9 Eigenvalue Analysis and Principal Component Analysis

9.1 Introduction

9.2 Eigen Analysis

9.3 Principal Component Analysis

9.4 Summary



10 Power Spectrum Analysis

10.1 Power Spectrum and Correlation

10.2 Fourier Series: Representation of Periodic Signals

10.3.3 Energy-Spectral Density and Power-Spectral Density

10.3 Fourier Transform: Representation of Aperiodic Signals

10.4 Non-Parametric Power Spectrum Estimation

10.5 Model-Based Power Spectral Estimation

10.6 High Resolution Spectral Estimation Based on Subspace Eigen-Analysis

10.7 Summary



11. Interpolation – Replacement of Lost Samples

11.1 Introduction

11.2 Model-Based Interpolation

11.3 Model-Based Interpolation

11.4 Summary



12 Signal Enhancement via Spectral Amplitude Estimation


12.2 Spectral Representation of Noisy Signals

12.3 Vector Representation of Spectrum of Noisy Signals

12.4 Spectral Subtraction

12.5 Bayesian MMSE Spectral Amplitude Estimation

12.6 Estimation of Signal to Noise Ratios

12.7 Application to Speech Restoration and Recognition

12.8 Summary



13 Impulsive Noise: Modelling, Detection and Removal

13.1 Impulsive Noise

13.2 Autocorrelation and Power Spectrum of Impulsive Noise

13.3 Probability Models for Impulsive Noise

13.4 Impulse contamination, Signal to Impulsive Noise Ratio

13.5 Median Filters

13.6 Impulsive Noise Removal Using Linear Prediction Models

13.7 Robust Parameter Estimation

13.8 Restoration of Archived Gramophone Records

13.9 Summary



14 Transient Noise Pulses

14.1 Transient Noise Waveforms

14.2 Transient Noise Pulse Models

14.3 Detection of Noise Pulses

14.4 Removal of Noise Pulse Distortions

14.5 Summary



15 Echo Cancellation

15.1 Introduction: Acoustic and Hybrid.i.Hybrid Echoes

15.2 Echo Return Time: The Sources of Delay in Communication Networks

15.3 Telephone Line Hybrid Echo

15.4 Hybrid Echo Suppression

15.5 .i.Adaptive Echo Cancellation

15.6 Acoustic .i.Echo

15.7 .i.Sub-band Acoustic Echo Cancellation

15.8 .i. Echo Cancellation with Linear Prediction Pre-whitening

15.9 Multiple-Input Multiple-Output (MIMO) Acoustic Echo Cancellation

15.10 Summary



16 Channel Equalisation and Blind Deconvolution

16.1 Introduction

16.2 Blind-Deconvolution Using Channel Input Power Spectrum

16.3 Equalisation Based on Linear Prediction Models

16.4 Bayesian Blind Deconvolution and Equalisation

16.5 Blind Equalisation for Digital Communication Channels

16.6 Equalisation Based on Higher-Order Statistics

16.7 Summary

16.8 Bibliography


17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement

17.1 An Overview of Speech Enhancement in Noise

17.2 Single-Input Speech Enhancement Methods

17.3 Speech Bandwidth Extension

17.4 Interpolation of Lost Speech Segments

17.5 Multiple-Input Speech Enhancement Methods

17.6 Speech Distortion Measurements

17.7 Summary

17.8 Bibliography


18 Multiple-Input Multiple-Output Systems, Independent Component Analysis

18.1 Introduction

18.2 MIMO Signal Propagation and Mixing Models

18.3 Independent Component Analysis

18.4 Summary



19 Signal Processing in Mobile Communication

19.1 Introduction to Cellular Communication

19.2 Communication Signal Processing in Mobile Systems

19.3 Noise, Capacity and Spectral Efficiency

19.4 Multi-path and Fading in Mobile Communication

19.5 Smart Beam-forming Antennas

19.6 Summary



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