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Nonlinear Source Separation [electronic resource] / by Luis B. Almeida.

By: Almeida, Luis B [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Signal Processing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2006Edition: 1st ed. 2006.Description: XII, 101 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031025266.Subject(s): Engineering | Electrical engineering | Signal processing | Technology and Engineering | Electrical and Electronic Engineering | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Introduction -- Linear Source Separation -- Nonlinear Separation -- Final Comments -- Statistical Concepts -- Online Software and Data.
In: Springer Nature eBookSummary: The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. Source separation deals with the problem of recovering sources that are observed in a mixed condition. When we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation is a relatively well studied subject, however nonlinear blind source separation is still in a less advanced stage, but has seen several significant developments in the last few years. This publication reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the MISEP, ensemble learning and kTDSEP methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years.
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Introduction -- Linear Source Separation -- Nonlinear Separation -- Final Comments -- Statistical Concepts -- Online Software and Data.

The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. Source separation deals with the problem of recovering sources that are observed in a mixed condition. When we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation is a relatively well studied subject, however nonlinear blind source separation is still in a less advanced stage, but has seen several significant developments in the last few years. This publication reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the MISEP, ensemble learning and kTDSEP methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years.

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