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Zustand: New. In English.

Zustand: Very Good. 448 pp., hardcover, highlighting and marginalia to portions of the text, else very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. …

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: Used. pp. 452 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 432 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: Used. pp. 452.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Independent Component Analysis | Theory and Applications | Te-Won Lee | Taschenbuch | xxxiv | Englisch | 2010 | Springer US | EAN 9781441950567 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Blind Speech Separation | Shoji Makino (u. a.) | Taschenbuch | Signals and Communication Technology | xvi | Englisch | 2010 | Springer | EAN 9789048176519 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.…

Zustand: gut. Blind Speech Separation (Signals and Communication Technology) In englischer Sprache. pages.

Paperback. Zustand: Brand New. 248 pages. 9.00x6.25x0.55 inches. In Stock.

Paperback. Zustand: Brand New. 447 pages. 9.25x6.00x1.01 inches. In Stock.