Subband Adaptive Filtering : Theory and Implementation

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  • Edition: CD
  • Format: Hardcover
  • Copyright: 2009-08-03
  • Publisher: Wiley

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This book provides an introductory but extensive guide to the theories behind various subband adaptive filtering techniques. The authors also provide MATLAB-based examples, applications and exercises to demonstrate the basic principles underlying subband adaptive filter production for beginners and people unused to working with subband adaptive filters. The comprehensive coverage of recent developments such as multi-width tap-weight adaptation, delayless architectures and filterbank design for band-edge effects enables advanced readers to keep up-to-date with the industry. A timely introduction for researchers, graduate students and engineers who want to design and deploy subband adaptive filters in their research and applications. Focuses on the important and advanced area of subband adaptive filtering, with information on new research directions and experiments. Includes ready-to-run MATLAB-based source programmes on the included CD, allowing readers to modify the code for different algorithms and applications and gain more insight into the theory and concepts of subband adaptive filters. Contains chapters on subband decomposition, orthogonality of multirate filter banks, critically-sampled and oversampled subband systems, multiband systems and delayless subband adaptive filters.

Author Biography

Kong-Aik Lee received his B.Eng (1st Class Hons) degree from Universiti Teknologi Malaysia in 1999, and his Ph.D. degree from Nanyang Technological University, Singapore, in 2006. He is currently a Research Fellow with the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A∗STAR), Singapore. He has been actively involved in the research on subband adaptive filtering techniques for the past few years. He invented the Multiband-structured Subband Adaptive Filter (MSAF), a very fast converging and computationally efficient subband adaptive filtering algorithm. His current research has primarily focused on improved classifier design for speaker and language recognition.

Woon-Seng Gan received his B.Eng (1st Class Hons) and PhD degrees, both in Electrical and Electronic Engineering from the University of Strathclyde, UK in 1989 and 1993 respectively. He joined the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, as a Lecturer and Senior Lecturer in 1993 and 1998 respectively. In 1999, he was promoted to Associate Professor. He is currently the Deputy Director of the Center for Signal Processing at Nanyang Technological University. His research interests include adaptive signal processing, psycho-acoustical signal processing, audio processing, and real-time embedded systems. He has published more than 170 international refereed journals and conference papers, and has been awarded four Singapore and US patents. He has previously co-authored two technical books on Digital Signal Processors: Architectures, Implementations, and Applications (Prentice Hall, 2005) and Embedded Signal Processing with the Micro Signal Architecture (Wiley-IEEE, 2007).
Dr. Gan has also won the Institute of Engineers Singapore (IES) Prestigious Engineering Achievement Award in 2001 for his work on Audio Beam System. He is currently serving as an Associate Editor for the EURASIP Journal on Audio, Speech and Music Processing, and EURASIP Research Letters in Signal Processing. He is also a Senior Member of IEEE and serves as a committee member in the IEEE Signal Processing Society Education Technical Committee.

Sen M. Kuo received the B.S. degree from National Taiwan Normal University, Taipei, Taiwan, in 1976 and the M.S. and Ph.D. degrees from the University of New Mexico, Albuquerque, NM in 1983 and 1985, respectively. He is a Professor and served as the department chair from 2002 to 2008 in the Department of Electrical Engineering, Northern Illinois University, DeKalb, IL. He was with Texas Instruments, Houston, TX in 1993, and with Chung-Ang University, Seoul, Korea in 2008. He is the leading author of four books: Active Noise Control Systems (Wiley, 1996), Real-Time Digital Signal Processing (Wiley, 2001, 2006), and Digital Signal Processors (Prentice Hall, 2005), and a co-author of Embedded Signal Processing with the Micro Signal Architecture (Wiley 2007). He holds seven US patents, and has published over 200 technical papers. His research focuses on active noise and vibration control, real-time DSP applications, adaptive echo and noise cancellation, digital audio and communication applications, and biomedical signal processing. Prof. Kuo received the IEEE first-place transactions (Consumer Electronics) paper award in 1993, and the faculty-of-year award in 2001 for accomplishments in research and scholarly areas. He served as an associate editor for IEEE Transactions on Audio, Speech and Language Processing, and serves as a member of the editorial boards for EURASIP Research Letters in Signal Processing and Journal of Electrical and Computer Engineering.

Table of Contents

About the authorsp. xi
Prefacep. xiii
Acknowledgmentsp. xv
List of symbolsp. xvii
List of abbreviationsp. xix
Introduction to adaptive filtersp. 1
Adaptive filteringp. 1
Adaptive transversal filtersp. 2
Performance surfacesp. 4
Adaptive algorithmsp. 6
Spectral dynamic range and misadjustmentp. 13
Applications of adaptive filtersp. 15
Adaptive system identificationp. 15
Adaptive predictionp. 23
Adaptive inverse modelingp. 25
Adaptive array processingp. 28
Summary of adaptive filtering applicationsp. 31
Transform-domain and subband adaptive filtersp. 31
Transform-domain adaptive filtersp. 31
Subband adaptive filtersp. 38
Summaryp. 39
Referencesp. 39
Subband decomposition and multirate systemsp. 41
Multirate systemsp. 41
Filter banksp. 44
Input-output relationp. 46
Perfect reconstruction filter banksp. 47
Polyphase representationp. 48
Paraunitary filter banksp. 54
Block transformsp. 55
Filter bank as a block transformp. 55
Cosine-modulated filter banksp. 59
Design examplep. 63
DFT filter banksp. 65
Design examplep. 66
A note on cosine modulationp. 67
Summaryp. 68
Referencesp. 69
Second-order characterization of multirate filter banksp. 73
Correlation-domain formulationp. 73
Critical decimationp. 77
Cross spectrump. 79
Subband spectrump. 82
Orthogonality at zero lagp. 85
Paraunitary conditionp. 86
Case study: Subband orthogonality of cosine-modulated filter banksp. 89
Correlation-domain analysisp. 89
MATLAB simulationsp. 92
Summaryp. 96
Referencesp. 97
Subband adaptive filtersp. 99
Subband adaptive filteringp. 99
Computational reductionp. 100
Spectral dynamic rangep. 101
Subband adaptive filter structuresp. 104
Open-loop structuresp. 104
Closed-loop structuresp. 104
Aliasing, band-edge effects and solutionsp. 106
Aliasing and band-edge effectsp. 107
Adaptive cross filtersp. 108
Multiband-structured SAFp. 110
Closed-loop delayless structuresp. 113
Delayless subband adaptive filtersp. 114
Closed-loop configurationp. 114
Open-loop configurationp. 115
Weight transformationp. 116
Computational requirementsp. 123
MATLAB examplesp. 124
Aliasing and band-edge effectsp. 125
Delayless alias-free SAFsp. 126
Summaryp. 128
Referencesp. 129
Critically sampled and oversampled subband structuresp. 133
Variants of critically sampled subband adaptive filtersp. 133
SAF with the affine projection algorithmp. 134
SAF with variable step sizesp. 136
SAF with selective coefficient updatep. 137
Oversampled and nonuniform subband adaptive filtersp. 138
Oversampled subband adaptive filteringp. 138
Nonuniform subband adaptive filteringp. 140
Filter bank designp. 141
Generalized DFT filter banksp. 141
Single-sideband modulation filter banksp. 142
Filter design criteria for DFT filter banksp. 144
Quadrature mirror filter banksp. 149
Pseudo-quadrature mirror filter banksp. 153
Conjugate quadrature filter banksp. 155
Case study: Proportionate subband adaptive filteringp. 156
Multiband structure with proportionate adaptationp. 156
MATLAB simulationsp. 157
Summaryp. 161
Referencesp. 163
Multiband-structured subband adaptive filtersp. 167
Multiband structurep. 167
Polyphase implementationp. 170
Multiband adaptationp. 173
Principle of minimal disturbancep. 173
Constrained subband updatesp. 173
Computational complexityp. 175
Underdetermined least-squares solutionsp. 177
NLMS equivalentp. 178
Projection interpretationp. 179
Stochastic interpretationsp. 179
Stochastic approximation to Newton's methodp. 179
Weighted MSE criterionp. 181
Decorrelating propertiesp. 186
Filter bank design issuesp. 187
The diagonal assumptionp. 187
Power complementary filter bankp. 187
The number of subbandsp. 188
Delayless MSAFp. 189
Open-loop configurationp. 189
Closed-loop configurationp. 191
MATLAB examplesp. 192
Convergence of the MSAF algorithmp. 193
Subband and time-domain constraintsp. 195
Summaryp. 198
Referencesp. 199
Stability and performance analysisp. 203
Algorithm, data model and assumptionsp. 203
The MSAF algorithmp. 203
Linear data modelp. 204
Paraunitary filter banksp. 206
Multiband MSE functionp. 209
MSE functionsp. 209
Excess MSEp. 210
Mean analysisp. 211
Projection interpretationp. 211
Mean behaviorp. 213
Mean-square analysisp. 214
Energy conservation relationp. 214
Variance relationp. 216
Stability of the MSAF algorithmp. 216
Steady-state excess MSEp. 217
MATLAB examplesp. 219
Mean of the projection matrixp. 219
Stability boundsp. 220
Steady-state excess MSEp. 222
Summaryp. 223
Referencesp. 224
New research directionsp. 227
Recent research on filter bank designp. 227
New SAF structures and algorithmsp. 228
In-band aliasing cancellationp. 228
Adaptive algorithms for the SAFp. 230
Variable tap lengths for the SAFp. 230
Theoretical analysisp. 232
Applications of the SAFp. 232
Further research on a multiband-structured SAFp. 233
Concluding remarksp. 234
Referencesp. 235
Programming in MATLABp. 241
MATLAB fundamentalsp. 241
Starting MATLABp. 241
Constructing and manipulating matricesp. 244
The colon operatorp. 244
Data typesp. 248
Working with stringsp. 248
Cell arrays and structuresp. 249
MATLAB scripting with M-filesp. 251
Plotting in MATLABp. 252
Other useful commands and tipsp. 255
Signal processing toolboxp. 258
Quick fact about the signal processing toolboxp. 258
Signal processing toolp. 262
Window design and analysis toolp. 267
Filter design toolboxp. 268
Quick fact about the filter design toolboxp. 268
Filter design and analysis toolp. 269
MATLAB functions for adaptive filteringp. 270
A case study: adaptive noise cancellationp. 272
Using MATLAB for adaptive filtering and subband adaptive filteringp. 279
Digital signal processingp. 279
Discrete-time signals and systemsp. 279
Signal representations in MATLABp. 280
Filtering and adaptive filtering in MATLABp. 282
FIR filteringp. 282
The LMS adaptive algorithmp. 284
Anatomy of the LMS code in MATLABp. 285
Multirate and subband adaptive filteringp. 292
Implementation of multirate filter banksp. 292
Implementation of a subband adaptive filterp. 297
Summary of MATLAB scripts, functions, examples and demosp. 301
Complexity analysis of adaptive algorithmsp. 307
Indexp. 317
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