Handbook of Blind Source Separation: Independent Component Analysis and Applications
A key task of engineers is to design and analyse systems; however, they often have to do this without knowing a system’s parameters. BSS is a very important area in signal processing as it enables engineers to derive the unknown inputs of a system from its known outputs. It also enables the separation of a set of signals from mixed set of signals. This is particularly important in telecommunications and biomedical engineering but is also key in speech, acoustic, audio and music processing and scientific data analysis. It is, therefore, a method that has wide applicability and is a very useful tool for many types of engineers and scientists in different fields.
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.
* The definitive reference: unlike other books, it contains the principles and all the major techniques and methods, cutting edge approaches and key application areas of Blind Source Separation
* Edited by the pioneers in the field with contributions from 34 of the world’s experts
* Describes all the main existing numerical algorithms and gives practical advice on their design
* 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
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Chapter 1 Introduction
Chapter 2 Information
Chapter 3 Contrasts
Chapter 4 Likelihood
Chapter 5 Algebraic methods after prewhitening
Chapter 6 Iterative algorithms
Chapter 7 Secondorder methods based on color
Chapter 8 Convolutive mixtures
Chapter 12 Bayesian approaches
Chapter 13 Nonnegative mixtures
Chapter 14 Nonlinear mixtures
Chapter 15 Semiblind methods for communications
Chapter 16 Overview of source separation applications
Chapter 17 Application to telecommunications
Chapter 18 Biomedical applications
Chapter 19 Audio applications
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algebraic algorithm applications approach approximation assume assumption Bayesian blind equalization blind separation Blind Signal Separation blind source separation channel chapter coefﬁcients columns complex computed considered constraint contrast function convergence convolutive mixtures correlation corresponding covariance covariance matrix criteria criterion cumulants cyclostationary decomposition deﬁned deﬁnition denoted distribution entropy equalization equations extraction FastICA ﬁeld ﬁlter ﬁnite ﬁrst frequency Gaussian identiﬁcation IEEE IEEE Trans IEEE Transactions images Independent Component Analysis instantaneous mixtures inverse iterative joint diagonalization Jutten kurtosis likelihood linear minimization mixing matrix mutual information Neural Networks noise non-Gaussian non-negative non-negative matrix factorization nonlinear number of sources observations obtained optimal orthogonal output parameters performance problem Proc random variables samples score function semi-blind Signal Processing Signal Separation solution source signals sparse spatial speciﬁc spectral statistical independence sufﬁcient t-f points techniques temporal tensor time-frequency Transactions on Signal transform update vector whitening