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Blind Speech Separation
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 theo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Dordrecht :
Springer Netherlands : Imprint: Springer,
2007.
|
| Edición: | 1st ed. 2007. |
| Colección: | Signals and Communication Technology,
|
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-1-4020-6479-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220120044251.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2007 ne | s |||| 0|eng d | ||
| 020 | |a 9781402064791 |9 978-1-4020-6479-1 | ||
| 024 | 7 | |a 10.1007/978-1-4020-6479-1 |2 doi | |
| 050 | 4 | |a TK5102.9 | |
| 072 | 7 | |a TJF |2 bicssc | |
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| 082 | 0 | 4 | |a 621.382 |2 23 |
| 245 | 1 | 0 | |a Blind Speech Separation |h [electronic resource] / |c edited by Shoji Makino, Te-Won Lee, Hiroshi Sawada. |
| 250 | |a 1st ed. 2007. | ||
| 264 | 1 | |a Dordrecht : |b Springer Netherlands : |b Imprint: Springer, |c 2007. | |
| 300 | |a XVI, 432 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Signals and Communication Technology, |x 1860-4870 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Telecommunication. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Communications Engineering, Networks. |
| 650 | 2 | 4 | |a Microwaves, RF Engineering and Optical Communications. |
| 700 | 1 | |a Makino, Shoji. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Lee, Te-Won. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Sawada, Hiroshi. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9789048115105 |
| 776 | 0 | 8 | |i Printed edition: |z 9789048176519 |
| 776 | 0 | 8 | |i Printed edition: |z 9781402064784 |
| 830 | 0 | |a Signals and Communication Technology, |x 1860-4870 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4020-6479-1 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||