Independent Component Analysis for Audio and Biosignal Applications
by Ganesh R Naik
Preface
Background and Motivation
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues.
This book presents theories and applications of ICA related to Audio and Biomedical signal processing applications and include invaluable examples of several real-world applications. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, and cumulant-based techniques are reviewed and put in an information theoretic framework to merge several lines of ICA research. The ICA algorithm has been successfully applied to many biomedical signalprocessing problems such as the analysis
of Electromyography (EMG), Electroencephalographic (EEG) data and functional
Magnetic Resonance Imaging (fMRI) data. The ICA algorithm can furthermore
be embedded in an expectation maximization framework for unsupervised classification.
It is also abundantly clear that ICA has been embraced by a number of researchers involved in Biomedical Signal processing as a powerful tool, which in many applications has supplanted decomposition methods such as Singular Value Decomposition (SVD). The book provides wide coverage of adaptive BSS techniques and algorithms both from the theoretical and practical point of view. The main objective is to derive and present efficient and simple adaptive algorithms that work well in practice for real-world Audio and Biomedical data.
This book is aimed to provide a self-contained introduction to the subject as well as offering a set of invited contributions, which we see as lying at the cutting edge of ICA research. ICA is intimately linked with the problem of Blind Source Separation (BSS) – attempting to recover a set of underlying sources when only a mapping from these sources, the observations, is given - and we regard this as canonical form of ICA. This book was created from discussions with researchers in the ICA community and aims to provide a snapshot of some current trends in ICA research.
InTeOp -- 2012 -- ISBN: 9535107828 9789535107828 -- 354 pages -- PDF -- 19 MB
Download
http://s18.alxa.net/s18/srvs2/02/003...t.Analysis.rar
by Ganesh R Naik
Preface
Background and Motivation
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues.
This book presents theories and applications of ICA related to Audio and Biomedical signal processing applications and include invaluable examples of several real-world applications. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, and cumulant-based techniques are reviewed and put in an information theoretic framework to merge several lines of ICA research. The ICA algorithm has been successfully applied to many biomedical signalprocessing problems such as the analysis
of Electromyography (EMG), Electroencephalographic (EEG) data and functional
Magnetic Resonance Imaging (fMRI) data. The ICA algorithm can furthermore
be embedded in an expectation maximization framework for unsupervised classification.
It is also abundantly clear that ICA has been embraced by a number of researchers involved in Biomedical Signal processing as a powerful tool, which in many applications has supplanted decomposition methods such as Singular Value Decomposition (SVD). The book provides wide coverage of adaptive BSS techniques and algorithms both from the theoretical and practical point of view. The main objective is to derive and present efficient and simple adaptive algorithms that work well in practice for real-world Audio and Biomedical data.
This book is aimed to provide a self-contained introduction to the subject as well as offering a set of invited contributions, which we see as lying at the cutting edge of ICA research. ICA is intimately linked with the problem of Blind Source Separation (BSS) – attempting to recover a set of underlying sources when only a mapping from these sources, the observations, is given - and we regard this as canonical form of ICA. This book was created from discussions with researchers in the ICA community and aims to provide a snapshot of some current trends in ICA research.
InTeOp -- 2012 -- ISBN: 9535107828 9789535107828 -- 354 pages -- PDF -- 19 MB
Download
http://s18.alxa.net/s18/srvs2/02/003...t.Analysis.rar