Blind Speech Separation: Signals and Communication Technology
Editat de Shoji Makino, Te-Won Lee, Hiroshi Sawadaen Limba Engleză Paperback – 30 noi 2010
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 901.45 lei 6-8 săpt. | |
SPRINGER NETHERLANDS – 30 noi 2010 | 901.45 lei 6-8 săpt. | |
Hardback (1) | 907.40 lei 6-8 săpt. | |
SPRINGER NETHERLANDS – 20 sep 2007 | 907.40 lei 6-8 săpt. |
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Specificații
ISBN-13: 9789048176519
ISBN-10: 9048176514
Pagini: 448
Ilustrații: XVI, 432 p.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.63 kg
Ediția:Softcover reprint of hardcover 1st ed. 2007
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Signals and Communication Technology
Locul publicării:Dordrecht, Netherlands
ISBN-10: 9048176514
Pagini: 448
Ilustrații: XVI, 432 p.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.63 kg
Ediția:Softcover reprint of hardcover 1st ed. 2007
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Signals and Communication Technology
Locul publicării:Dordrecht, Netherlands
Public țintă
ResearchCuprins
Multiple Microphone Blind Speech Separation with ICA.- Convolutive Blind Source Separation for Audio Signals.- Frequency-Domain Blind Source Separation.- Blind Source Separation using Space–Time Independent Component Analysis.- TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation.- SIMO-Model-Based Blind Source Separation – Principle and its Applications.- Independent Vector Analysis for Convolutive Blind Speech Separation.- Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation.- Underdetermined Blind Speech Separation with Sparseness.- The DUET Blind Source Separation Algorithm.- K-means Based Underdetermined Blind Speech Separation.- Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization.- Bayesian Audio Source Separation.- Single Microphone Blind Speech Separation.- Monaural Source Separation.- Probabilistic Decompositions of Spectra for Sound Separation.- Sparsification for Monaural Source Separation.- Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods.
Notă biografică
Dr. Shoji Makino is an IEEE Fellow, Associate Editor of the IEEE Transactions on Speech & Audio Processing, and Executive Manager NTT Communication Science Laboratories. Dr. Makino was also co-editor on the succesful 2005 Springer book: Benesty - Speech Enhancement.
Textul de pe ultima copertă
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.
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.
Caracteristici
cutting edge topic on blind source separation top researchers from all over the world tutorial in nature and in-depth treatment