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Advanced Digital Signal Processing and Noise Reduction

ISBN-10: 0471626929

ISBN-13: 9780471626923

Edition: 2nd 2000 (Revised)

Authors: Saeed V. Vaseghi

List price: $145.00
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Description:

This edition provides a broad-ranging presentation of theory and applications of statistical signal processing. It has been enhanced by much new material, such as illustrative examples and more visual illustrations.
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Book details

List price: $145.00
Edition: 2nd
Copyright year: 2000
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/20/2000
Binding: Hardcover
Pages: 498
Size: 7.25" wide x 10.00" long x 1.25" tall
Weight: 2.134
Language: English

Preface
Symbols
Abbreviations
Introduction
Signals and Information
Signal Processing Methods
Transform-based Signal Processing
Model-based Signal Processing
Bayesian Signal Processing
Neural Networks
Applications of Digital Signal Processing
Adaptive Noise Cancellation
Adaptive Noise Reduction
Blind Channel Equalisation
Signal Classification and Pattern Recognition
Linear Prediction Modelling of Speech
Digital Coding of Audio Signals
Detection of Signals in Noise
Directional Reception of Waves: Beam-forming
Dolby Noise Reduction
Radar Signal Processing: Doppler Frequency Shift
Sampling and Analogue-to-digital Conversion
Sampling and Reconstruction of Analogue Signals
Quantisation
Bibliography
Noise and Distortion
Introduction
White Noise
Band-limited White Noise
Coloured Noise
Impulsive Noise
Transient Noise Pulses
Thermal Noise
Shot Noise
Electromagnetic Noise
Channel Distortions
Echo and Multipath Reflections
Modelling Noise
Additive White Gaussian Noise Model
Hidden Markov Model for Noise
Bibliography
Probability and Information Models
Introduction
Random Signals
Random and Stochastic Processes
The Space of a Random Process
Probability Models
Probability and Random Variables
Probability Mass Function
Probability Density Function
Probability Dgnsity Functions of Random Processes
Information Models
Entropy
Mutual Information
Entropy Coding
Stationary and Nonstationary Random Processes
Strict-sense Stationary Processes
Wide-sense Stationary Processes
Nonstationary Processes
Statistics (Expected Values) of a Random Process
The Mean Value
Autocorrelation
Autocovariance
Power Spectral Density
Joint Statistical Averages of Two Random Processes
Cross-correlation and Cross-covariance
Cross-power Spectral Density and Coherence
Ergodic Processes and Time-averaged Statistics
Mean-ergodic Processes
Correlation-ergodic Processes
Some Useful Classes of Random Processes
Gaussian (Normal) Process
Multivariate Gaussian Process
Mixture Gaussian Process
A Binary-state Gaussian Process
Poisson Process
Shot Noise
Poisson-Gaussian Model for Clutters and Impulsive Noise
Markov Processes
Markov Chain Processes
Gamma Probability Distribution
Rayleigh Probability Distribution
Laplacian Probability Distribution
Transformation of a Random Process
Monotonic Transformation of Random Processes
Many-to-one Mapping of Random Signals
Summary
Bibliography
Bayesian Inference
Bayesian Estimation Theory: Basic Definitions
Dynamic and Probability Models in Estimation
Parameter Space and Signal Space
Parameter Estimation and Signal Restoration
Performance Measures and Desirable Properties of Estimators
Prior and Posterior Spaces and Distributions
Bayesian Estimation
Maximum a Posteriori Estimation
Maximum-likelihood Estimation
Minimum Mean Square Error Estimation
Minimum Mean Absolute Value of Error Estimation
Equivalence of the MAP, ML, MMSE and MAVE for Gaussian Processes with Uniform Distributed Parameters
The Influence of the Prior on Estimation Bias and Variance
The Relative Importance of the Prior and the Observation
The Estimate-Maximise Method
Convergence of the EM Algorithm
Cramer-Rao Bound on the Minimum Estimator Variance
Cramer-Rao Bound for Random Parameters
Cramer-Rao Bound for a Vector Parameter
Design of Gaussian Mixture Models
EM Estimation of Gaussian Mixture Model
Bayesian Classification
Binary Classification
Classification Error
Bayesian Classification of Discrete-valued Parameters
Maximum a Posteriori Classification
Maximum-likelihood Classification
Minimum Mean Square Error Classification
Bayesian Classification of Finite State Processes
Bayesian Estimation of the Most Likely State Sequence
Modelling the Space of a Random Process
Vector Quantisation of a Random Process
Vector Quantisation using Gaussian Models
Design of a Vector Quantiser: K-means Clustering
Summary
Bibliography
Hidden Markov Models
Statistical Models for Nonstationary Processes
Hidden Markov Models
Comparison of Markov and Hidden Markov Models
A Physical Interpretation: HMMs of Speech
Hidden Markov Model as a Bayesian Model
Parameters of a Hidden Markov Model
State Observation Probability Models
State Transition Probabilities
State-Time Trellis Diagram
Training Hidden Markov Models
Forward-Backward Probability Computation
Baum-Welch Model Re-estimation
Training HMMs with Discrete Density Observation Models
HMMs with Continuous Density Observation Models
HMMs with Gaussian Mixture pdfs
Decoding of Signals using Hidden Markov Models
Viterbi Decoding Algorithm
HMMs in DNA and Protein Sequence Modelling
HMMs for Modelling Speech and Noise
Modelling Speech with HMMs
HMM-based Estimation of Signals in Noise
Signal and Noise Model Combination and Decomposition
Hidden Markov Model Combination
Decomposition of State Sequences of Signal and Noise
HMM-based Wiener Filters
Modelling Noise Characteristics
Summary
Bibliography
Least Square Error Filters
Least Square Error Estimation: Wiener Filters
Block-data Formulation of the Wiener Filter
QR Decomposition of the Least Square Error Equation
Interpretation of Wiener Filters as Projections in Vector Space
Analysis of the Least Mean Square Error Signal
Formulation of Wiener Filters in the Frequency Domain
Some Applications of Wiener Filters
Wiener Filters for Additive Noise Reduction
Wiener Filters and Separability of Signal and Noise
The Square-root Wiener Filter
Wiener Channel Equaliser
Time-alignment of Signals in Multichannel/Multisensor Systems
Implementation of Wiener Filters
The Choice of Wiener Filter Order
Improvements to Wiener Filters
Summary
Bibliography
Adaptive Filters
Introduction
State-space Kalman Filters
Derivation of the Kalman Filter Algorithm
Sample-adaptive Filters
Recursive Least Square Adaptive Filters
The Matrix Inversion Lemma
Recursive Time-update of Filter Coefficients
The Steepest-descent Method
Convergence Rate
Vector-valued Adaptation Step Size
The LMS Filter
Leaky LMS Algorithm
Normalised LMS Algorithm
Summary
Bibliography
Linear Prediction Models
Linear Prediction Coding
Frequency Response of LP Models
Calculation of Predictor Coefficients
Effect of Estimation of Correlation Function on LP Model Solution
The Inverse Filter: Spectral Whitening
The Prediction Error Signal
Forward, Backward and Lattice Predictors
Augmented Equations for Forward and Backward Predictors
Levinson-Durbin Recursive Solution
Lattice Predictors
Alternative Formulations of Least Square Error Prediction
Predictor Model Order Selection
Short- and Long-term Predictors
MAP Estimation of Predictor Coefficients
Probability Density Function of Predictor Output
Using the Prior pdf of the Predictor Coefficients
Formant-tracking LP Models
Sub-band Linear Prediction Model
Signal Restoration using Linear Prediction Models
Frequency-domain Signal Restoration using Prediction Models
Implementation of Sub-band Linear Prediction Wiener Filters
Summary
Bibliography
Power Spectrum and Correlation
Power Spectrum and Correlation
Fourier Series: Representation of Periodic Signals
Fourier Transform: Representation of Aperiodic Signals
Discrete Fourier Transform
Time/Frequency Resolutions, the Uncertainty Principle
Energy-spectral Density and Power-spectral Density
Nonparametric Power Spectrum Estimation
The Mean and Variance of Periodograms
Averaging Periodograms (Bartlett Method)
Welch Method: Averaging Periodograms from Overlapped and Windowed Segments
Blackman-Tukey Method
Power Spectrum Estimation from Autocorrelation of Overlapped Segments
Model-based Power Spectrum Estimation
Maximum-entropy Spectral Estimation
Autoregressive Power Spectrum Estimation
Moving-average Power Spectrum Estimation
Autoregressive Moving-average Power Spectrum Estimation
High-resolution Spectral Estimation Based on Subspace Eigenanalysis
Pisarenko Harmonic Decomposition
Multiple Signal Classification Spectral Estimation
Estimation of Signal Parameters via Rotational Invariance Techniques
Summary
Bibliography
Interpolation
Introduction
Interpolation of a Sampled Signal
Digital Interpolation by a Factor of I
Interpolation of a Sequence of Lost Samples
The Factors that affect Interpolation Accuracy
Polynomial Interpolation
Lagrange Polynomial Interpolation
Newton Polynomial Interpolation
Hermite Polynomial Interpolation
Cubic Spline Interpolation
Model-based Interpolation
Maximum a Posteriori Interpolation
Least Square Error Autoregressive Interpolation
Interpolation based on a Short-term Prediction Model
Interpolation based on Long- and Short-term Correlations
LSAR Interpolation Error
Interpolation in Frequency-Time Domain
Interpolation using Adaptive Codebooks
Interpolation through Signal Substitution
Summary
Bibliography
Spectral Amplitude Estimation
Introduction
Spectral Representation of Noisy Signals
Vector Representation of the Spectrum of Noisy Signals
Spectral Subtraction
Power Spectrum Subtraction
Magnitude Spectrum Subtraction
Spectral Subtraction Filter: Relation to Wiener Filters
Processing Distortions
Effect of Spectral Subtraction on Signal Distribution
Reducing the Noise Variance
Filtering Out the Processing Distortions
Nonlinear Spectra] Subtraction
Implementation of Spectral Subtraction
Bayesian MMSE Spectral Amplitude Estimation
Application to Speech Restoration and Recognition
Summary
Bibliography
Impulsive Noise
Impulsive Noise
Autocorrelation and Power Spectrum of Impulsive Noise
Statistical Models for Impulsive Noise
Bernoulli-Gaussian Model of Impulsive Noise
Poisson-Gaussian Model of Impulsive Noise
A Binary-state Model of Impulsive Noise
Signal-to-impulsive-noise Ratio
Median Filters
Impulsive Noise Removal using Linear Prediction Models
Impulsive Noise Detection
Analysis of Improvement in Noise Detectability
Two-sided Predictor for Impulsive Noise Detection
Interpolation of Discarded Samples
Robust Parameter Estimation
Restoration of Archived Gramophone Records
Summary
Bibliography
Transient Noise Pulses
Transient Noise Waveforms
Transient Noise Pulse Models
Noise Pulse Templates
Autoregressive Model of Transient Noise Pulses
Hidden Markov Model of a Noise Pulse Process
Detection of Noise Pulses
Matched Filter for Noise Pulse Detection
Noise Detection based on Inverse Filtering
Noise Detection based on HMM
Removal of Noise Pulse Distortions
Adaptive Subtraction of Noise Pulses
AR-based Restoration of Signals Distorted by Noise Pulses
Summary
Bibliography
Echo Cancellation
Introduction: Acoustic and Hybrid Echoes
Telephone Line Hybrid Echo
Echo: the Sources of Delay in Telephone Networks
Echo Return Loss
Hybrid Echo Suppression
Adaptive Echo Cancellation
Echo Canceller Adaptation Methods
Convergence of Line Echo Canceller
Echo Cancellation for Digital Data Transmission
Acoustic Echo
Sub-band Acoustic Echo Cancellation
Multiple-input Multiple-output Echo Cancellation
Stereophonic Echo Cancellation Systems
Summary
Bibliography
Channel Equalisation and Blind Deconvolution
Introduction
The Ideal Inverse Channel Filter
Equalisation Error, Convolutional Noise
Blind Equalisation
Minimum- and Maximum-phase Channels
Wiener Equaliser
Blind Equalisation using the Channel Input Power Spectrum
Homomorphic Equalisation
Homomorphic Equalisation using a Bank of High-pass Filters
Equalisation based on Linear Prediction Models
Blind Equalisation through Model Factorisation
Bayesian Blind Deconvolution and Equalisation
Conditional Mean Channel Estimation
Maximum-likelihood Channel Estimation
Maximum a Posteriori Channel Estimation
Channel Equalisation based on Hidden Markov Models
MAP Channel Estimate based on HMMs
Implementations of HMM-based Deconvolution
Blind Equalisation for Digital Communications Channels
LMS Blind Equalisation
Equalisation of a Binary Digital Channel
Equalisation based on Higher-order Statistics
Higher-order Moments, Cumulants and Spectra
Higher-order Spectra of Linear Time-invariant Systems
Blind Equalisation based on Higher-order Cepstra
Summary
Bibliography
Speech Enhancement in Noise
Introduction
Single-input Speech-enhancement Methods
An Overview of a Speech-enhancement System
Wiener Filter for De-noising Speech
Spectra] Subtraction of Noise
Bayesian MMSE Speech Enhancement
Kalman Filter
Speech Enhancement via LP Model Reconstruction
Multiple-input Speech-enhancement Methods
Beam-forming with Microphone Arrays
Speech Distortion Measurements
Bibliography
Noise in Wireless Communications
Introduction to Cellular Communications
Noise, Capacity and Spectral Efficiency
Communications Signal Processing in Mobile Systems
Noise and Distortion in Mobile Communications Systems
Multipath Propagation of Electromagnetic Signals
Rake Receivers for Multipath Signals
Signal Fading in Mobile Communications Systems
Large-scale Signal Fading
Small-scale Fast Signal Fading
Smart Antennas
Switched and Adaptive Smart Antennas
Space-Time Signal Processing - Diversity Schemes
Summary
Bibliography
Index