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More About This Title Advanced Digital Signal Processing and Noise Reduction 4e
English
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.
English
English
Preface xix
Acknowledgements xxiii
Symbols xxv
Abbreviations xxix
1 Introduction 1
1.1 Signals, Noise and Information 1
1.2 Signal Processing Methods 3
1.3 Applications of Digital Signal Processing 6
1.4 A Review of Sampling and Quantisation 22
1.5 Summary 32
Bibliography 32
2 Noise and Distortion 35
2.1 Introduction 35
2.2 White Noise 37
2.3 Coloured Noise; Pink Noise and Brown Noise 39
2.4 Impulsive and Click Noise 39
2.5 Transient Noise Pulses 41
2.6 Thermal Noise 41
2.7 Shot Noise 42
2.8 Flicker (I/f ) Noise 43
2.9 Burst Noise 44
2.10 Electromagnetic (Radio) Noise 45
2.11 Channel Distortions 46
2.12 Echo and Multi-path Reflections 47
2.13 Modelling Noise 47
Bibliography 50
3 Information Theory and Probability Models 51
3.1 Introduction: Probability and Information Models 52
3.2 Random Processes 53
3.3 Probability Models of Random Signals 57
3.4 Information Models 64
3.5 Stationary and Non-Stationary Random Processes 73
3.6 Statistics (Expected Values) of a Random Process 76
3.7 Some Useful Practical Classes of Random Processes 87
3.8 Transformation of a Random Process 98
3.9 Search Engines: Citation Ranking 103
3.10 Summary 104
Bibliography 105
4 Bayesian Inference 107
4.1 Bayesian Estimation Theory: Basic Definitions 108
4.2 Bayesian Estimation 117
4.3 Expectation-Maximisation (EM) Method 128
4.4 Cramer–Rao Bound on the Minimum Estimator Variance 131
4.5 Design of Gaussian Mixture Models (GMMs) 134
4.6 Bayesian Classification 136
4.7 Modelling the Space of a Random Process 143
4.8 Summary 145
Bibliography 146
5 Hidden Markov Models 147
5.1 Statistical Models for Non-Stationary Processes 147
5.2 Hidden Markov Models 149
5.3 Training Hidden Markov Models 155
5.4 Decoding Signals Using Hidden Markov Models 161
5.5 HMMs in DNA and Protein Sequences 164
5.6 HMMs for Modelling Speech and Noise 165
5.7 Summary 171
Bibliography 171
6 Least Square ErrorWiener-Kolmogorov Filters 173
6.1 Least Square Error Estimation:Wiener-Kolmogorov Filter 173
6.2 Block-Data Formulation of theWiener Filter 178
6.3 Interpretation ofWiener Filter as Projection in Vector Space 179
6.4 Analysis of the Least Mean Square Error Signal 181
6.5 Formulation ofWiener Filters in the Frequency Domain 182
6.6 Some Applications ofWiener Filters 183
6.7 Implementation ofWiener Filters 188
6.8 Summary 191
Bibliography 191
7 Adaptive Filters: Kalman, RLS, LMS 193
7.1 Introduction 194
7.2 State-Space Kalman Filters 195
7.3 Extended Kalman Filter (EFK) 206
7.4 Unscented Kalman Filter (UFK) 208
7.5 Sample Adaptive Filters – LMS, RLS 211
7.6 Recursive Least Square (RLS) Adaptive Filters 213
7.7 The Steepest-Descent Method 217
7.8 Least Mean Squared Error (LMS) Filter 220
7.9 Summary 223
Bibliography 224
8 Linear Prediction Models 227
8.1 Linear Prediction Coding 227
8.2 Forward, Backward and Lattice Predictors 236
8.3 Short-Term and Long-Term Predictors 243
8.4 MAP Estimation of Predictor Coefficients 245
8.5 Formant-Tracking LP Models 247
8.6 Sub-Band Linear Prediction Model 248
8.7 Signal Restoration Using Linear Prediction Models 249
8.8 Summary 254
Bibliography 254
9 Eigenvalue Analysis and Principal Component Analysis 257
9.1 Introduction – Linear Systems and Eigen Analysis 257
9.2 Eigen Vectors and Eigenvalues 261
9.3 Principal Component Analysis (PCA) 264
9.4 Summary 269
Bibliography 270
10 Power Spectrum Analysis 271
10.1 Power Spectrum and Correlation 271
10.2 Fourier Series: Representation of Periodic Signals 272
10.3 Fourier Transform: Representation of Non-periodic Signals 274
10.4 Non-Parametric Power Spectrum Estimation 279
10.5 Model-Based Power Spectrum Estimation 283
10.6 High-Resolution Spectral Estimation Based on Subspace Eigen-Analysis 287
10.7 Summary 293
Bibliography 293
11 Interpolation – Replacement of Lost Samples 295
11.1 Introduction 295
11.2 Polynomial Interpolation 301
11.3 Model-Based Interpolation 306
11.4 Summary 319
Bibliography 319
12 Signal Enhancement via Spectral Amplitude Estimation 321
12.1 Introduction 321
12.2 Spectral Subtraction 324
12.3 Bayesian MMSE Spectral Amplitude Estimation 333
12.4 Estimation of Signal to Noise Ratios 335
12.5 Application to Speech Restoration and Recognition 336
12.6 Summary 338
Bibliography 338
13 Impulsive Noise: Modelling, Detection and Removal 341
13.1 Impulsive Noise 341
13.2 Autocorrelation and Power Spectrum of Impulsive Noise 344
13.3 Probability Models of Impulsive Noise 345
13.4 Impulsive Noise Contamination, Signal to Impulsive Noise Ratio 349
13.5 Median Filters for Removal of Impulsive Noise 350
13.6 Impulsive Noise Removal Using Linear Prediction Models 351
13.7 Robust Parameter Estimation 355
13.8 Restoration of Archived Gramophone Records 357
13.9 Summary 358
Bibliography 358
14 Transient Noise Pulses 359
14.1 Transient NoiseWaveforms 359
14.2 Transient Noise Pulse Models 361
14.3 Detection of Noise Pulses 364
14.4 Removal of Noise Pulse Distortions 366
14.5 Summary 369
Bibliography 369
15 Echo Cancellation 371
15.1 Introduction: Acoustic and Hybrid Echo 371
15.2 Echo Return Time: The Sources of Delay in Communication Networks 373
15.3 Telephone Line Hybrid Echo 375
15.4 Hybrid (Telephone Line) Echo Suppression 377
15.5 Adaptive Echo Cancellation 377
15.6 Acoustic Echo 381
15.7 Sub-Band Acoustic Echo Cancellation 384
15.8 Echo Cancellation with Linear Prediction Pre-whitening 385
15.9 Multi-Input Multi-Output Echo Cancellation 386
15.10 Summary 389
Bibliography 389
16 Channel Equalisation and Blind Deconvolution 391
16.1 Introduction 391
16.2 Blind Equalisation Using Channel Input Power Spectrum 398
16.3 Equalisation Based on Linear Prediction Models 400
16.4 Bayesian Blind Deconvolution and Equalisation 402
16.5 Blind Equalisation for Digital Communication Channels 409
16.6 Equalisation Based on Higher-Order Statistics 414
16.7 Summary 420
Bibliography 421
17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement 423
17.1 An Overview of Speech Enhancement in Noise 424
17.2 Single-Input Speech Enhancement Methods 425
17.3 Speech Bandwidth Extension–Spectral Extrapolation 442
17.4 Interpolation of Lost Speech Segments–Packet Loss Concealment 447
17.5 Multi-Input Speech Enhancement Methods 455
17.6 Speech Distortion Measurements 462
Bibliography 464
18 Multiple-Input Multiple-Output Systems, Independent Component Analysis 467
18.1 Introduction 467
18.2 A note on comparison of beam-forming arrays and ICA 469
18.3 MIMO Signal Propagation and Mixing Models 469
18.4 Independent Component Analysis 472
18.5 Summary 490
Bibliography 490
19 Signal Processing in Mobile Communication 491
19.1 Introduction to Cellular Communication 491
19.2 Communication Signal Processing in Mobile Systems 497
19.3 Capacity, Noise, and Spectral Efficiency 498
19.4 Multi-path and Fading in Mobile Communication 500
19.5 Smart Antennas – Space–Time Signal Processing 505
19.6 Summary 508
Bibliography 508
Index 509
