Book description
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation.
- Covers the principles and major techniques and methods in one book
- Edited by the pioneers in the field with contributions from 34 of the world’s experts
- Describes the main existing numerical algorithms and gives practical advice on their design
- Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications
- Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- About the editors
- Preface
- Contributors
- Chapter 1. Introduction
- Chapter 2. Information
- Chapter 3. Contrasts
- Chapter 4. Likelihood
- Chapter 5. Algebraic methods after prewhitening
-
Chapter 6. Iterative algorithms
- 6.1 Introduction
- 6.2 Model and goal
- 6.3 Contrast functions for iterative BSS/ICA
- 6.4 Iterative search algorithms: Generalities
- 6.5 Iterative whitening
- 6.6 Classical adaptive algorithms
- 6.7 Relative (natural) gradient techniques
- 6.8 Adapting the nonlinearities
- 6.9 Iterative algorithms based on deflation
- 6.10 The FastICA algorithm
- 6.11 Iterative algorithms with optimal step size
- 6.12 Summary, conclusions and outlook
- References
- Chapter 7. Second-order methods based on color
- Chapter 8. Convolutive mixtures
- Chapter 9. Algebraic identification of under-determined mixtures
- Chapter 10. Sparse component analysis
- Chapter 11. Quadratic time-frequency domain methods
-
Chapter 12. Bayesian approaches
- 12.1 Introduction
- 12.2 Source separation forward model and notations
- 12.3 General Bayesian scheme
- 12.4 Relation to PCA and ICA
- 12.5 Prior and likelihood assignments
- 12.6 Source modeling
- 12.7 Estimation schemes
- 12.8 Source separation applications
- 12.9 Source characterization
- 12.10 Conclusion
- References
- Chapter 13. Non-negative mixtures
-
Chapter 14. Nonlinear mixtures
- 14.1 Introduction
- 14.2 Nonlinear ICA in the general case
- 14.3 ICA for constrained nonlinear mixtures
- 14.4 Priors on sources
- 14.5 Independence criteria
- 14.6 A Bayesian approach for general mixtures
- 14.7 Other methods and algorithms
- 14.8 A few applications
- 14.9 Conclusion
- Acknowledgments
- Software
- References
-
Chapter 15. Semi-blind methods for communications
- 15.1 Introduction
- 15.2 Training-based and blind equalization
- 15.3 Overcoming the limitations of blind methods
- 15.4 Mathematical formulation
- 15.5 Channel equalization criteria
- 15.6 Algebraic equalizers
- 15.7 Iterative equalizers
- 15.8 Performance analysis
- 15.9 Semi-blind channel estimation
- 15.10 Summary, conclusions and outlook
- References
-
Chapter 16. Overview of source separation applications
- 16.1 Introduction
- 16.2 How to solve an actual source separation problem
- 16.3 Overfitting and robustness
- 16.4 Illustration with electromagnetic transmission systems
- 16.5 Example: Analysis of Mars hyperspectral images
- 16.6 Mono- vs multi-dimensional sources and mixtures
- 16.7 Using physical mixture models or not
- 16.8 Some conclusions and available tools
- References
-
Chapter 17. Application to telecommunications
- 17.1 Introduction
- 17.2 Data model, statistics and problem formulation
- 17.3 Possible methods
- 17.4 Ultimate separators of instantaneous mixtures
- 17.5 Blind separators of instantaneous mixtures
- 17.6 Instantaneous approach versus convolutive approach: simulation results
- 17.7 Conclusion
- Acknowledgment
- References
- Chapter 18. Biomedical applications
- Chapter 19. Audio applications
- Glossary
- Subject Index
Product information
- Title: Handbook of Blind Source Separation
- Author(s):
- Release date: February 2010
- Publisher(s): Elsevier Science
- ISBN: 9780080884943
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