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...
Salvato in:
| Altri autori: | , , |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Dordrecht :
Springer Netherlands
2007.
Cham : Springer Nature |
| Serie: | Signals and Communication Technology
|
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Blind Speech Separation, Texte imprimé, 9781402064784 • Blind Speech Separation, Texte imprimé, 9789048115105 • Blind Speech Separation, Texte imprimé, 9789048176519 |
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| 008 | 080410s2007 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN12315104X | ||
| 020 | |a 9781402064791 | ||
| 020 | |a 9781402064791 | ||
| 041 | 0 | |a eng | |
| 082 | |a 621.382 | ||
| 245 | 0 | 0 | |a Blind speech separation |c edited by Shoji Makino, Hiroshi Sawada, Te-Won Lee. |
| 260 | |a Dordrecht : |b Springer Netherlands. | ||
| 260 | |a Cham : |b Springer Nature, |c 2007. | ||
| 490 | 0 | |a Signals and Communication Technology |x 1860-4870 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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. | ||
| 700 | 1 | |a Makino, Shoji. |4 pbd | |
| 700 | 1 | |a Lee, Te-Won. |4 pbd | |
| 700 | 1 | |a Sawada, Hiroshi. |4 pbd | |
| 776 | 0 | |t Blind Speech Separation |b Texte imprimé |z 9781402064784 | |
| 776 | 0 | |t Blind Speech Separation |b Texte imprimé |z 9789048115105 | |
| 776 | 0 | |t Blind Speech Separation |b Texte imprimé |z 9789048176519 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-1-4020-6479-1 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-L0234LVW-L |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:747887314 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-1-4020-6479-1 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:750900334 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-1-4020-6479-1 |z Accès INSA CVL | |
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