Subband Adaptive Filtering
Theory and Implementation
Published on 26. June 2009
Book
Hardback
344 pages
978-0-470-51694-2 (ISBN)
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Description
Subband adaptive filtering is rapidly becoming one of the most effective techniques for reducing computational complexity and improving the convergence rate of algorithms in adaptive signal processing applications. This book provides an introductory, yet extensive guide on the theory of various subband adaptive filtering techniques. For beginners, the authors discuss the basic principles that underlie the design and implementation of subband adaptive filters. For advanced readers, a comprehensive coverage of recent developments, such as multiband tap-weight adaptation, delayless architectures, and filter-bank design methods for reducing band-edge effects are included. Several analysis techniques and complexity evaluation are also introduced in this book to provide better understanding of subband adaptive filtering. This book bridges the gaps between the mixed-domain natures of subband adaptive filtering techniques and provides enough depth to the material augmented by many MATLAB(r) functions and examples.
Key Features:
* Acts as a timely introduction for researchers, graduate students and engineers who want to design and deploy subband adaptive filters in their research and applications.
* Bridges the gaps between two distinct domains: adaptive filter theory and multirate signal processing.
* Uses a practical approach through MATLAB(r)-based source programs on the accompanying CD.
* Includes more than 100 M-files, allowing readers to modify the code for different algorithms and applications and to gain more insight into the theory and concepts of subband adaptive filters.
Subband Adaptive Filtering is aimed primarily at practicing engineers, as well as senior undergraduate and graduate students. It will also be of interest to researchers, technical managers, and computer scientists.
More details
Persons
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.
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.
Content
About the authors. Preface.
Acknowledgments.
List of symbols.
List of abbreviations.
1 Introduction to adaptive filters.
1.1 Adaptive filtering.
1.2 Adaptive transversal filters.
1.3 Performance surfaces.
1.4 Adaptive algorithms.
1.5 Spectral dynamic range and misadjustment.
1.6 Applications of adaptive filters.
1.7 Transform-domain and subband adaptive filters.
1.8 Summary.
References.
2 Subband decomposition and multirate systems.
2.1 Multirate systems.
2.2 Filter banks.
2.3 Paraunitary filter banks.
2.4 Block transforms
2.5 Cosine-modulated filter banks.
2.6 DFT filter banks.
2.7 A note on cosine modulation.
2.8 Summary.
References 69.
3 Second-order characterization of multirate filter banks.
3.1 Correlation-domain formulation.
3.2 Cross spectrum.
3.3 Orthogonality at zero lag.
3.4 Case study: Subband orthogonality of cosine-modulated filter banks.
3.5 Summary.
References.
4 Subband adaptive filters.
4.1 Subband adaptive filtering.
4.2 Subband adaptive filter structures.
4.3 Aliasing, band-edge effects and solutions.
4.4 Delayless subband adaptive filters.
4.5 MATLAB examples.
4.6 Summary.
References.
5 Critically sampled and oversampled subband structures.
5.1 Variants of critically sampled subband adaptive filters.
5.2 Oversampled and nonuniform subband adaptive filters.
5.3 Filter bank design.
5.4 Case study: Proportionate subband adaptive filtering.
5.5 Summary.
References.
6 Multiband-structured subband adaptive filters.
6.1 Multiband structure.
6.2 Multiband adaptation.
6.3 Underdetermined least-squares solutions.
6.4 Stochastic interpretations.
6.5 Filter bank design issues.
6.6 Delayless MSAF.
6.7 MATLAB examples.
6.8 Summary.
References.
7 Stability and performance analysis.
7.1 Algorithm, data model and assumptions.
7.2 Multiband MSE function.
7.3 Mean analysis.
7.4 Mean-square analysis.
7.5 MATLAB examples.
7.6 Summary.
References.
8 New research directions.
8.1 Recent research on filter bank design.
8.2 New SAF structures and algorithms.
8.3 Theoretical analysis.
8.4 Applications of the SAF.
8.5 Further research on a multiband-structured SAF.
8.6 Concluding remarks.
References.
Appendix A Programming in MATLAB.
A.1 MATLAB fundamentals.
A.2 Signal processing toolbox.
A.3 Filter design toolbox.
Appendix B Using MATLAB for adaptive filtering and subband adaptive filtering.
B.1 Digital signal processing.
B.2 Filtering and adaptive filtering in MATLAB.
B.3 Multirate and subband adaptive filtering.
Appendix C Summary of MATLAB scripts, functions, examples and demos.
Appendix D Complexity analysis of adaptive algorithms.
Index.
Acknowledgments.
List of symbols.
List of abbreviations.
1 Introduction to adaptive filters.
1.1 Adaptive filtering.
1.2 Adaptive transversal filters.
1.3 Performance surfaces.
1.4 Adaptive algorithms.
1.5 Spectral dynamic range and misadjustment.
1.6 Applications of adaptive filters.
1.7 Transform-domain and subband adaptive filters.
1.8 Summary.
References.
2 Subband decomposition and multirate systems.
2.1 Multirate systems.
2.2 Filter banks.
2.3 Paraunitary filter banks.
2.4 Block transforms
2.5 Cosine-modulated filter banks.
2.6 DFT filter banks.
2.7 A note on cosine modulation.
2.8 Summary.
References 69.
3 Second-order characterization of multirate filter banks.
3.1 Correlation-domain formulation.
3.2 Cross spectrum.
3.3 Orthogonality at zero lag.
3.4 Case study: Subband orthogonality of cosine-modulated filter banks.
3.5 Summary.
References.
4 Subband adaptive filters.
4.1 Subband adaptive filtering.
4.2 Subband adaptive filter structures.
4.3 Aliasing, band-edge effects and solutions.
4.4 Delayless subband adaptive filters.
4.5 MATLAB examples.
4.6 Summary.
References.
5 Critically sampled and oversampled subband structures.
5.1 Variants of critically sampled subband adaptive filters.
5.2 Oversampled and nonuniform subband adaptive filters.
5.3 Filter bank design.
5.4 Case study: Proportionate subband adaptive filtering.
5.5 Summary.
References.
6 Multiband-structured subband adaptive filters.
6.1 Multiband structure.
6.2 Multiband adaptation.
6.3 Underdetermined least-squares solutions.
6.4 Stochastic interpretations.
6.5 Filter bank design issues.
6.6 Delayless MSAF.
6.7 MATLAB examples.
6.8 Summary.
References.
7 Stability and performance analysis.
7.1 Algorithm, data model and assumptions.
7.2 Multiband MSE function.
7.3 Mean analysis.
7.4 Mean-square analysis.
7.5 MATLAB examples.
7.6 Summary.
References.
8 New research directions.
8.1 Recent research on filter bank design.
8.2 New SAF structures and algorithms.
8.3 Theoretical analysis.
8.4 Applications of the SAF.
8.5 Further research on a multiband-structured SAF.
8.6 Concluding remarks.
References.
Appendix A Programming in MATLAB.
A.1 MATLAB fundamentals.
A.2 Signal processing toolbox.
A.3 Filter design toolbox.
Appendix B Using MATLAB for adaptive filtering and subband adaptive filtering.
B.1 Digital signal processing.
B.2 Filtering and adaptive filtering in MATLAB.
B.3 Multirate and subband adaptive filtering.
Appendix C Summary of MATLAB scripts, functions, examples and demos.
Appendix D Complexity analysis of adaptive algorithms.
Index.