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 separ… Meer...
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.; PDF; Scientific, Technical and Medical > Electronics & communications engineering > Communications engine, Springer Netherlands<
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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 sepa… Meer...
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. Books > Engineering eBook, Springer Shop<
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(*) Uitverkocht betekent dat het boek is momenteel niet beschikbaar op elk van de bijbehorende platforms we zoeken.
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 separ… Meer...
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.; PDF; Scientific, Technical and Medical > Electronics & communications engineering > Communications engine, Springer Netherlands<
No. 9781402064791. Verzendingskosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten., exclusief verzendingskosten
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 sepa… Meer...
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. Books > Engineering eBook, Springer Shop<
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Bibliografische gegevens van het best passende boek
Boek bevindt zich in het datenbestand sinds 2010-07-17T19:23:32+02:00 (Amsterdam) Detailpagina laatst gewijzigd op 2023-12-21T13:25:01+01:00 (Amsterdam) ISBN/EAN: 1402064799
ISBN - alternatieve schrijfwijzen: 1-4020-6479-9, 978-1-4020-6479-1 alternatieve schrijfwijzen en verwante zoekwoorden: Auteur van het boek: hiroshi sawada, shoji, won lee Titel van het boek: separation
Gegevens van de uitgever
Auteur: Shoji Makino; Te-Won Lee; Hiroshi Sawada Titel: Signals and Communication Technology; Blind Speech Separation Uitgeverij: Springer; Springer Netherland 432 Bladzijden Verschijningsjaar: 2007-09-07 Dordrecht; NL Taal: Engels 149,79 € (DE) 154,00 € (AT) 177,00 CHF (CH) Available XVI, 432 p.
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. cutting edge topic on blind source separation top researchers from all over the world tutorial in nature and in-depth treatment
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