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Source separation and machine learning /

Chien, Jen-Tzung,

Source separation and machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Source separation and machine learning /Jen-Tzung Chien.
作者:	Chien, Jen-Tzung,
面頁冊數:	xxx, 353 pages :illustrations ;24 cm.
標題:	Blind source separation.
ISBN:	9780128177969

Source separation and machine learning /
Chien, Jen-Tzung,

Source separation and machine learning /Jen-Tzung Chien. - xxx, 353 pages :illustrations ;24 cm.

Includes bibliographical references and index.

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation.

ISBN: 9780128177969$99.95

Nat. Bib. Agency Control No.: cam 2200265 i 4500Subjects--Topical Terms:

340034
Blind source separation.

LC Class. No.: TK5102.9 / .C45 2019

Source separation and machine learning /
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050 4 $a TK5102.9 $b .C45 2019
100 1 $a Chien, Jen-Tzung, $e author. $3 857087
245 1 0 $a Source separation and machine learning / $c Jen-Tzung Chien.
264 1 $a London, United Kingdom : $b Academic Press, $c [2019]
264 4 $c ©2019.
300 $a xxx, 353 pages : $b illustrations ; $c 24 cm.
336 $a text $b txt $2 rdacontent.
337 $a unmediated $b n $2 rdamedia.
338 $a volume $b nc $2 rdacarrier.
504 $a Includes bibliographical references and index.
520 $a Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation.
650 0 $a Blind source separation. $3 340034
650 0 $a Machine learning. $3 188639