Advanced digital signal processing and noise reduction

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Advanced digital signal processing and noise reduction / Saeed V. Vaseghi

版:: 2nd ed
資料種別:: 図書
出版情報:: Chichester : John Wiley & Sons, c2000
形態:: xxiii, 473 p. ; 25 cm
著者名:: Vaseghi, Saeed V. <DA10136144>
ISBN:: 9780471626923 [0471626929]
書誌ID:: BA47833783

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注記:

Includes bibliographies and index
"First edition published in 1996 jointly by John Wiley and B.G.Teubner as Advanced signal processing and digital noise reduction" -- T.p. verso

主題:

Digital filters (Mathematics); Electronic noise; Signal processing

言語:

英語

目次情報:

Preface

Symbols

Abbreviations

Introduction / 1：

Signals and Information / 1.1：

Signal Processing Methods / 1.2：

Transform-based Signal Processing / 1.2.1：

Model-based Signal Processing / 1.2.2：

Bayesian Signal Processing / 1.2.3：

Neural Networks / 1.2.4：

Applications of Digital Signal Processing / 1.3：

Adaptive Noise Cancellation / 1.3.1：

Adaptive Noise Reduction / 1.3.2：

Blind Channel Equalisation / 1.3.3：

Signal Classification and Pattern Recognition / 1.3.4：

Linear Prediction Modelling of Speech / 1.3.5：

Digital Coding of Audio Signals / 1.3.6：

Detection of Signals in Noise / 1.3.7：

Directional Reception of Waves: Beam-forming / 1.3.8：

Dolby Noise Reduction / 1.3.9：

Radar Signal Processing: Doppler Frequency Shift / 1.3.10：

Sampling and Analogue-to-digital Conversion / 1.4：

Sampling and Reconstruction of Analogue Signals / 1.4.1：

Quantisation / 1.4.2：

Bibliography

Noise and Distortion / 2：

White Noise / 2.1：

Band-limited White Noise / 2.2.1：

Coloured Noise / 2.3：

Impulsive Noise / 2.4：

Transient Noise Pulses / 2.5：

Thermal Noise / 2.6：

Shot Noise / 2.7：

Electromagnetic Noise / 2.8：

Channel Distortions / 2.9：

Echo and Multipath Reflections / 2.10：

Modelling Noise / 2.11：

Additive White Gaussian Noise Model / 2.11.1：

Hidden Markov Model for Noise / 2.11.2：

Probability and Information Models / 3：

Random Signals / 3.1：

Random and Stochastic Processes / 3.2.1：

The Space of a Random Process / 3.2.2：

Probability Models / 3.3：

Probability and Random Variables / 3.3.1：

Probability Mass Function / 3.3.2：

Probability Density Function / 3.3.3：

Probability Dgnsity Functions of Random Processes / 3.3.4：

Information Models / 3.4：

Entropy / 3.4.1：

Mutual Information / 3.4.2：

Entropy Coding / 3.4.3：

Stationary and Nonstationary Random Processes / 3.5：

Strict-sense Stationary Processes / 3.5.1：

Wide-sense Stationary Processes / 3.5.2：

Nonstationary Processes / 3.5.3：

Statistics (Expected Values) of a Random Process / 3.6：

The Mean Value / 3.6.1：

Autocorrelation / 3.6.2：

Autocovariance / 3.6.3：

Power Spectral Density / 3.6.4：

Joint Statistical Averages of Two Random Processes / 3.6.5：

Cross-correlation and Cross-covariance / 3.6.6：

Cross-power Spectral Density and Coherence / 3.6.7：

Ergodic Processes and Time-averaged Statistics / 3.6.8：

Mean-ergodic Processes / 3.6.9：

Correlation-ergodic Processes / 3.6.10：

Some Useful Classes of Random Processes / 3.7：

Gaussian (Normal) Process / 3.7.1：

Multivariate Gaussian Process / 3.7.2：

Mixture Gaussian Process / 3.7:3：

A Binary-state Gaussian Process / 3.7.4：

Poisson Process / 3.7.5：

Poisson-Gaussian Model for Clutters and Impulsive Noise / 3.7.6：

Markov Processes / 3.7.8：

Markov Chain Processes / 3.7.9：

Gamma Probability Distribution / 3.7.10：

Rayleigh Probability Distribution / 3.7.11：

Laplacian Probability Distribution / 3.7.12：

Transformation of a Random Process / 3.8：

Monotonic Transformation of Random Processes / 3.8.1：

Many-to-one Mapping of Random Signals / 3.8.2：

Summary / 3.9：

Bayesian Inference / 4：

Bayesian Estimation Theory: Basic Definitions / 4.1：

Dynamic and Probability Models in Estimation / 4.1.1：

Parameter Space and Signal Space / 4.1.2：

Parameter Estimation and Signal Restoration / 4.1.3：

Performance Measures and Desirable Properties of Estimators / 4.1.4：

Prior and Posterior Spaces and Distributions / 4.1.5：

Bayesian Estimation / 4.2：

Maximum a Posteriori Estimation / 4.2.1：

Maximum-likelihood Estimation / 4.2.2：

Minimum Mean Square Error Estimation / 4.2.3：

Minimum Mean Absolute Value of Error Estimation / 4.2.4：

Equivalence of the MAP, ML, MMSE and MAVE for Gaussian Processes with Uniform Distributed Parameters / 4.2.5：

The Influence of the Prior on Estimation Bias and Variance / 4.2.6：

The Relative Importance of the Prior and the Observation / 4.2.7：

The Estimate-Maximise Method / 4.3：

Convergence of the EM Algorithm / 4.3.1：

Cramer-Rao Bound on the Minimum Estimator Variance / 4.4：

Cramer-Rao Bound for Random Parameters / 4.4.1：

Cramer-Rao Bound for a Vector Parameter / 4.4.2：

Design of Gaussian Mixture Models / 4.5：

EM Estimation of Gaussian Mixture Model / 4.5.1：

Bayesian Classification / 4.6：

Binary Classification / 4.6.1：

Classification Error / 4.6.2：

Bayesian Classification of Discrete-valued Parameters / 4.6.3：

Maximum a Posteriori Classification / 4.6.4：

Maximum-likelihood Classification / 4.6.5：

Minimum Mean Square Error Classification / 4.6.6：

Bayesian Classification of Finite State Processes / 4.6.7：

Bayesian Estimation of the Most Likely State Sequence / 4.6.8：

Modelling the Space of a Random Process / 4.7：

Vector Quantisation of a Random Process / 4.7.1：

Vector Quantisation using Gaussian Models / 4.7.2：

Design of a Vector Quantiser: K-means Clustering / 4.7.3：

Hidden Markov Models / 4.8：

Statistical Models for Nonstationary Processes / 5.1：

Comparison of Markov and Hidden Markov Models / 5.2：

A Physical Interpretation: HMMs of Speech / 5.2.2：

Hidden Markov Model as a Bayesian Model / 5.2.3：

Parameters of a Hidden Markov Model / 5.2.4：

State Observation Probability Models / 5.2.5：

State Transition Probabilities / 5.2.6：

State-Time Trellis Diagram / 5.2.7：

Training Hidden Markov Models / 5.3：

Forward-Backward Probability Computation / 5.3.1：

Baum-Welch Model Re-estimation / 5.3.2：

Training HMMs with Discrete Density Observation Models / 5.3.3：

HMMs with Continuous Density Observation Models / 5.3.4：

HMMs with Gaussian Mixture pdfs / 5.3.5：

Decoding of Signals using Hidden Markov Models / 5.4：

Viterbi Decoding Algorithm / 5.4.1：

HMMs in DNA and Protein Sequence Modelling / 5.5：

HMMs for Modelling Speech and Noise / 5.6：

Modelling Speech with HMMs / 5.6.1：

HMM-based Estimation of Signals in Noise / 5.6.2：

Signal and Noise Model Combination and Decomposition / 5.6.3：

Hidden Markov Model Combination / 5.6.4：

Decomposition of State Sequences of Signal and Noise / 5.6.5：

HMM-based Wiener Filters / 5.6.6：

Modelling Noise Characteristics / 5.6.7：

Least Square Error Filters / 5.7：

Least Square Error Estimation: Wiener Filters / 6.1：

Block-data Formulation of the Wiener Filter / 6.2：

QR Decomposition of the Least Square Error Equation / 6.2.1：

Interpretation of Wiener Filters as Projections in Vector Space / 6.3：

Analysis of the Least Mean Square Error Signal / 6.4：

Formulation of Wiener Filters in the Frequency Domain / 6.5：

Some Applications of Wiener Filters / 6.6：

Wiener Filters for Additive Noise Reduction / 6.6.1：

Wiener Filters and Separability of Signal and Noise / 6.6.2：

The Square-root Wiener Filter / 6.6.3：

Wiener Channel Equaliser / 6.6.4：

Time-alignment of Signals in Multichannel/Multisensor Systems / 6.6.5：

Implementation of Wiener Filters / 6.7：

The Choice of Wiener Filter Order / 6.7.1：

Improvements to Wiener Filters / 6.7.2：

Adaptive Filters / 6.8：

State-space Kalman Filters / 7.1：

Derivation of the Kalman Filter Algorithm / 7.2.1：

Sample-adaptive Filters / 7.3：

Recursive Least Square Adaptive Filters / 7.4：

The Matrix Inversion Lemma / 7.4.1：

Recursive Time-update of Filter Coefficients / 7.4.2：

The Steepest-descent Method / 7.5：

Convergence Rate / 7.5.1：

Vector-valued Adaptation Step Size / 7.5.2：

The LMS Filter / 7.6：

Leaky LMS Algorithm / 7.6.1：

Normalised LMS Algorithm / 7.6.2：

Linear Prediction Models / 7.7：

Linear Prediction Coding / 8.1：

Frequency Response of LP Models / 8.1.1：

Calculation of Predictor Coefficients / 8.1.2：

Effect of Estimation of Correlation Function on LP Model Solution / 8.1.3：

The Inverse Filter: Spectral Whitening / 8.1.4：

The Prediction Error Signal / 8.1.5：

Forward, Backward and Lattice Predictors / 8.2：

Augmented Equations for Forward and Backward Predictors / 8.2.1：

Levinson-Durbin Recursive Solution / 8.2.2：

Lattice Predictors / 8.2.3：

Alternative Formulations of Least Square Error Prediction / 8.2.4：

Predictor Model Order Selection / 8.2.5：

Short- and Long-term Predictors / 8.3：

MAP Estimation of Predictor Coefficients / 8.4：

Probability Density Function of Predictor Output / 8.4.1：

Using the Prior pdf of the Predictor Coefficients / 8.4.2：

Formant-tracking LP Models / 8.5：

Sub-band Linear Prediction Model / 8.6：

Signal Restoration using Linear Prediction Models / 8.7：

Frequency-domain Signal Restoration using Prediction Models / 8.7.1：

Implementation of Sub-band Linear Prediction Wiener Filters / 8.7.2：

Power Spectrum and Correlation / 8.8：

Fourier Series: Representation of Periodic Signals / 9.1：

Fourier Transform: Representation of Aperiodic Signals / 9.3：

Discrete Fourier Transform / 9.3.1：

Time/Frequency Resolutions, the Uncertainty Principle / 9.3.2：

Energy-spectral Density and Power-spectral Density / 9.3.3：

Nonparametric Power Spectrum Estimation / 9.4：

The Mean and Variance of Periodograms / 9.4.1：

Averaging Periodograms (Bartlett Method) / 9.4.2：

Welch Method: Averaging Periodograms from Overlapped and Windowed Segments / 9.4.3：

Blackman-Tukey Method / 9.4.4：

Power Spectrum Estimation from Autocorrelation of Overlapped Segments / 9.4.5：

Model-based Power Spectrum Estimation / 9.5：

Maximum-entropy Spectral Estimation / 9.5.1：

Autoregressive Power Spectrum Estimation / 9.5.2：

Moving-average Power Spectrum Estimation / 9.5.3：

Autoregressive Moving-average Power Spectrum Estimation / 9.5.4：

High-resolution Spectral Estimation Based on Subspace Eigenanalysis / 9.6：

Pisarenko Harmonic Decomposition / 9.6.1：

Multiple Signal Classification Spectral Estimation / 9.6.2：

Estimation of Signal Parameters via Rotational Invariance Techniques / 9.6.3：

Interpolation / 9.7：

Interpolation of a Sampled Signal / 10.1：

Digital Interpolation by a Factor of I / 10.1.2：

Interpolation of a Sequence of Lost Samples / 10.1.3：

The Factors that affect Interpolation Accuracy / 10.1.4：

Polynomial Interpolation / 10.2：

Lagrange Polynomial Interpolation / 10.2.1：

Newton Polynomial Interpolation / 10.2.2：

Hermite Polynomial Interpolation / 10.2.3：

Cubic Spline Interpolation / 10.2.4：

Model-based Interpolation / 10.3：

Maximum a Posteriori Interpolation / 10.3.1：

Least Square Error Autoregressive Interpolation / 10.3.2：

Interpolation based on a Short-term Prediction Model / 10.3.3：

Interpolation based on Long- and Short-term Correlations / 10.3.4：

LSAR Interpolation Error / 10.3.5：

Interpolation in Frequency-Time Domain / 10.3.6：

Interpolation using Adaptive Codebooks / 10.3.7：

Interpolation through Signal Substitution / 10.3.8：

Spectral Amplitude Estimation / 10.4：

Spectral Representation of Noisy Signals / 11.1：

Vector Representation of the Spectrum of Noisy Signals / 11.1.2：

Spectral Subtraction / 11.2：

Power Spectrum Subtraction / 11.2.1：

Magnitude Spectrum Subtraction / 11.2.2：

Spectral Subtraction Filter: Relation to Wiener Filters / 11.2.3：

Processing Distortions / 11.2.4：

Effect of Spectral Subtraction on Signal Distribution / 11.2.5：

Reducing the Noise Variance / 11.2.6：

Filtering Out the Processing Distortions / 11.2.7：

Nonlinear Spectra] Subtraction / 11.2.8：

Implementation of Spectral Subtraction / 11.2.9：

Bayesian MMSE Spectral Amplitude Estimation / 11.3：

Application to Speech Restoration and Recognition / 11.4：

Autocorrelation and Power Spectrum of Impulsive Noise / 11.5：

Statistical Models for Impulsive Noise / 12.2：

Bernoulli-Gaussian Model of Impulsive Noise / 12.2.1：

Poisson-Gaussian Model of Impulsive Noise / 12.2.2：

A Binary-state Model of Impulsive Noise / 12.2.3：

Signal-to-impulsive-noise Ratio / 12.2.4：

Median Filters / 12.3：

Impulsive Noise Removal using Linear Prediction Models / 12.4：

Impulsive Noise Detection / 12.4.1：

Analysis of Improvement in Noise Detectability / 12.4.2：

Two-sided Predictor for Impulsive Noise Detection / 12.4.3：

Interpolation of Discarded Samples / 12.4.4：

Robust Parameter Estimation / 12.5：

Restoration of Archived Gramophone Records / 12.6：

Transient Noise Waveforms / 12.7：

Transient Noise Pulse Models / 13.2：

Noise Pulse Templates / 13.2.1：

Autoregressive Model of Transient Noise Pulses / 13.2.2：

Hidden Markov Model of a Noise Pulse Process / 13.2.3：

Detection of Noise Pulses / 13.3：

Matched Filter for Noise Pulse Detection / 13.3.1：

Noise Detection based on Inverse Filtering / 13.3.2：

Noise Detection based on HMM / 13.3.3：

Removal of Noise Pulse Distortions / 13.4：

Adaptive Subtraction of Noise Pulses / 13.4.1：

AR-based Restoration of Signals Distorted by Noise Pulses / 13.4.2：

Echo Cancellation / 13.5：

Introduction: Acoustic and Hybrid Echoes / 14.1：

Telephone Line Hybrid Echo / 14.2：

Echo: the Sources of Delay in Telephone Networks / 14.2.1：

Echo Return Loss / 14.2.2：

Hybrid Echo Suppression / 14.3：

Adaptive Echo Cancellation / 14.4：

Echo Canceller Adaptation Methods / 14.4.1：

Convergence of Line Echo Canceller / 14.4.2：

Echo Cancellation for Digital Data Transmission / 14.4.3：

Acoustic Echo / 14.5：

Sub-band Acoustic Echo Cancellation / 14.6：

Multiple-input Multiple-output Echo Cancellation / 14.7：

Stereophonic Echo Cancellation Systems / 14.7.1：

Channel Equalisation and Blind Deconvolution / 14.8：

The Ideal Inverse Channel Filter / 15.1：

Equalisation Error, Convolutional Noise / 15.1.2：

Blind Equalisation / 15.1.3：

Minimum- and Maximum-phase Channels / 15.1.4：

Wiener Equaliser / 15.1.5：

Blind Equalisation using the Channel Input Power Spectrum / 15.2：

Homomorphic Equalisation / 15.2.1：

Homomorphic Equalisation using a Bank of High-pass Filters / 15.2.2：

Equalisation based on Linear Prediction Models / 15.3：

Blind Equalisation through Model Factorisation / 15.3.1：

Bayesian Blind Deconvolution and Equalisation / 15.4：

Conditional Mean Channel Estimation / 15.4.1：

Maximum-likelihood Channel Estimation / 15.4.2：

Maximum a Posteriori Channel Estimation / 15.4.3：

Channel Equalisation based on Hidden Markov Models / 15.4.4：

MAP Channel Estimate based on HMMs / 15.4.5：

Implementations of HMM-based Deconvolution / 15.4.6：

Blind Equalisation for Digital Communications Channels / 15.5：

LMS Blind Equalisation / 15.5.1：

Equalisation of a Binary Digital Channel / 15.5.2：

Equalisation based on Higher-order Statistics / 15.6：

Higher-order Moments, Cumulants and Spectra / 15.6.1：

Higher-order Spectra of Linear Time-invariant Systems / 15.6.2：

Blind Equalisation based on Higher-order Cepstra / 15.6.3：

Speech Enhancement in Noise / 15.7：

Single-input Speech-enhancement Methods / 16.1：

An Overview of a Speech-enhancement System / 16.2.1：

Wiener Filter for De-noising Speech / 16.2.2：

Spectra] Subtraction of Noise / 16.2.3：

Bayesian MMSE Speech Enhancement / 16.2.4：

Kalman Filter / 16.2.5：

Speech Enhancement via LP Model Reconstruction / 16.2.6：

Multiple-input Speech-enhancement Methods / 16.3：

Beam-forming with Microphone Arrays / 16.3.1：

Speech Distortion Measurements / 16.4：

Noise in Wireless Communications / 17：

Introduction to Cellular Communications / 17.1：

Noise, Capacity and Spectral Efficiency / 17.2：

Communications Signal Processing in Mobile Systems / 17.3：

Noise and Distortion in Mobile Communications Systems / 17.4：

Multipath Propagation of Electromagnetic Signals / 17.4.1：

Rake Receivers for Multipath Signals / 17.4.2：

Signal Fading in Mobile Communications Systems / 17.4.3：

Large-scale Signal Fading / 17.4.4：

Small-scale Fast Signal Fading / 17.4.5：

Smart Antennas / 17.5：

Switched and Adaptive Smart Antennas / 17.5.1：

Space-Time Signal Processing - Diversity Schemes / 17.5.2：

Index / 17.6：

Preface

Symbols

Abbreviations

Introduction / 1：

Signals and Information / 1.1：

Signal Processing Methods / 1.2：

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