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Blind Speech Separation Papeback -
by Shoji Makino (Editor); Te-Won Lee (Editor); Hiroshi Sawada (Editor)
- New
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Details
- Title Blind Speech Separation
- Author Shoji Makino (Editor); Te-Won Lee (Editor); Hiroshi Sawada (Editor)
- Binding Papeback
- Edition Softcover reprin
- Condition New
- Pages 432
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date pp. 450
- Bookseller's Inventory # 614404663
- ISBN 9789048176519 / 9048176514
- Weight 1.38 lbs (0.63 kg)
- Dimensions 9.21 x 6.14 x 0.91 in (23.39 x 15.60 x 2.31 cm)
- Category Technology & Industrial Arts
- Dewey Decimal Code 621.382
- Quantity available 4
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From the publisher
From the rear cover
This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. 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. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques.
Blind Speech Separation is divided into three parts:
Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. This approach utilizes spatial diversity to discriminate between desired and undesired components, i.e., it reduces the undesired components by forming a spatial null towards them. It is, in fact, a blind adaptive beamformer realized by unsupervised adaptive filtering.
Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane.
Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.
