Blind Speech Separation (eBook)

Shoji Makino, Te-Won Lee, Hiroshi Sawada (Herausgeber)

eBook Download: PDF

2007 | 2007
XVI, 432 Seiten
Springer Netherland (Verlag)
978-1-4020-6479-1 (ISBN)

Infos

This is the world's first edited book on independent component analysis (ICA)-based blind source separation (BSS) of convolutive mixtures of speech. This book brings together a small number of leading researchers to provide tutorial-like and in-depth treatment on major ICA-based BSS topics, with the objective of becoming the definitive source for current, comprehensive, authoritative, and yet accessible treatment.

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.

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We are surrounded by sounds. Such a noisy environment makes it di?cult to obtain desired speech and it is di?cult to converse comfortably there. This makes it important to be able to separate and extract a target speech signal from noisy observations for both man-machine and human-human communication. Blindsourceseparation(BSS)isanapproachforestimatingsourcesignals using only information about their mixtures observed in each input channel. The estimation is performed without possessing information on each source, such as its frequency characteristics and location, or on how the sources are mixed. The use of BSS in the development of comfortable acoustic com- nication channels between humans and machines is widely accepted. Some books have been published on BSS, independent component ana- sis (ICA), and related subjects. There, ICA-based BSS has been well studied in the statistics and information theory ?elds, for applications to a variety of disciplines including wireless communication and biomedicine. However, as speech and audio signal mixtures in a real reverberant environment are generally convolutive mixtures, they involve a structurally much more ch- lenging task than instantaneous mixtures, which are prevalent in many other applications.

Inhalt

Part I: Multiple Microphone Blind Speech Separation with ICA 1. Convolutive Blind Source Separation for Speech Signals; S.C.Douglas, M.Gupta.
2. Frequency-Domain Blind Source Separation; H.Sawada, S.Araki, S.Makino.
3. Blind Source Separation using Space-Time Independent Component Analysis; M.Davies, et al.
4. TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation; H.Buchner, R.Aichner, W.Kellermann.
5. SIMO-Model-Based Blind Source Separation-principle and its applications; H.Saruwatari, T.Takatani, K.Shikano.
6. Independent Vector Analysis for Convolutive Blind Speech Separation; I.Lee, T.Kim, T-W.Lee.
7. Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation; M.Zibulevsky. Part II: Underdeterminded Blind Speech Separation with Sparseness 8. The DUET Blind Source Separation Algorithm; S.Rickard.
9. K-means Based Underdetermined Blind Speech Separation; S.Araki, H.Sawada, S.Makino.
10. Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization; S.Winter, et al.
11. Bayesian Audio Source Separation; C.Févotte. Part III: Single Microphone Blind Speech Separation 12. Monaural Source Separation; G.J.Jang, T-W.Lee.
13. Probabilistic Decompositions of Spectra for Sound Separation; P.Smaragdis.
14. Sparsification for Monaural Source Separation; H.Asari, et al.
15. Monaural Speech Separation by Support Vector Machines; S.Hochreiter, M.C.Mozer.
Index.

Produkt-Details

• Erscheint lt. Verlag: 7.9.2007
• Reihe/Serie: Signals and Communication Technology
• Reihe/Serie: Signals and Communication Technology
• Zusatzinfo: XVI, 432 p.
• Verlagsort: Dordrecht
• Sprache: englisch
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• Schlagworte: Adaptive Filter • algorithm • algorithms • Blind Source Separation • Clustering • filtering • Filters • Independent Component Analysis • Optimization • sparse component analysis • Support Vector Machine • System Identification • Unsupervised Learning
• ISBN-10: 1-4020-6479-9 / 1402064799
• ISBN-13: 978-1-4020-6479-1 / 9781402064791