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Unsupervised Adaptive Filtering, Volume 1: Blind Source SeparationFrom Wiley-Interscience
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A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Topics in Volume I include:
* Neural and information-theoretic approaches to blind signal separation
* Models, concepts, algorithms, and performance of blind source separation
* Blind separation of delayed and convolved sources
* Blind deconvolution of multipath mixtures
* Applications of blind source separation
Volume II: Blind Deconvolution continues coverage with blind channel equalization and its relationship to blind source separation.
- Sales Rank: #3132712 in Books
- Published on: 2000-04-14
- Original language: English
- Number of items: 1
- Dimensions: 9.55" h x 1.06" w x 6.38" l, .0 pounds
- Binding: Hardcover
- 446 pages
Review
"Contributions are by fore-most experts, and provides up-to-date research findings." (IEE Signal Processing Magazine, Vol. 18, No. 1, January 2001)
From the Back Cover
A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Topics in Volume I include:
- Neural and information-theoretic approaches to blind signal separation
- Models, concepts, algorithms, and performance of blind source separation
- Blind separation of delayed and convolved sources
- Blind deconvolution of multipath mixtures
- Applications of blind source separation
About the Author
JULIUS S. BENDAT, PhD, is President of the J. S. Bendat Company and the author of Nonlinear System Techniques and Applications (available from Wiley). ALLAN G. PIERSOL, PE, is President of Piersol Engineering Company and the author of several chapters in engineering handbooks. The authors have previously collaborated on the companion volume to this book, Engineering Applications of Correlation and Spectral Analysis, Second Edition, also available from Wiley.
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Most helpful customer reviews
4 of 5 people found the following review helpful.
Advances in Unsupervised Adaptive Filtering
By A Customer
This book is essential for anyone who wishes to stay informed about new developments in the very important field of signal processing. In the introduction, Simon Haykin (of Canada's McMaster University) explains the classification of supervised and unsupervised forms of adaptive filtering and the three fundamental approaches to unsupervised adaptive filtering. He also explains that blind deconvolution (the subject of volume 2) involves inverse problems with both similarities and differences vis-a-vis blind source separation. In an important way, volume one is an indispensable introduction to the second volume.
The contributors to volume one are an impressive collection from both academic research and the private sector. Haykin has assembled an international team from institutions and private laboratories in France, Japan and the United States. Their contributions are both theoretical and practical. The chapters include references to important literature in signal processing, communications and control, to facilitate further research.
The first seven chapters are information-theoretic approaches. The final two chapters present important practical issues in solving problems with delayed or convolved mixtures of independent source signals. Chapter Nine (by R. Lambert and C. Nikias) is especially useful, as it presents a powerful and novel tool for separating real-life speech.
This book is a significant contribution to the signal processing literature and deserves a place on the shelf of anyone interested in adaptive filtering.
2 of 3 people found the following review helpful.
A Great book for this field..
By Amazon Customer
Succinct and clear explanations, broad coverages and the most recent examples like functional MRI.
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