Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010200141 | TK5102.9 V37 2008 | Open Access Book | Book | Searching... |
On Order
Summary
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 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 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.
Author Notes
SAEED V. VASEGHI , Brunel University, UK
Table of Contents
| Contents |
| Symbols |
| Abbreviations |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 4 Baseyian Inference |
| 4.1 Bayesian Estimation Theory: Basic Definitions |
| 4.2 Bayesian Estimation |
| 4.3 The Estimate-Maximise Method |
| 4.4 CramerûRao 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 9 Eigenvalue Analysis and Principal Component Analysis |
| 9.1 Introduction |
| 9.2 Eigen Analysis |
| 9.3 Principal Component Analysis |
| 9.4 Summary |
| Bibliography |
| 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 |
| Bibliography |
| 11 Interpolation û Replacement of Lost Samples |
| 11.1 Introduction |
| 11.2 Model-Based Interpolation |
| 11.3 Model-Based Interpolation |
| 11.4 Summary |
| Bibliography |
| 12 Signal Enhancement via Spectral Amplitude Estimation |
| 12.1Introduction |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| 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 |
| Bibliography |
| Index |
