Source Separation and Machine Learning

Nonfiction, Science & Nature, Technology, Electronics, Engineering
Cover of the book Source Separation and Machine Learning by Jen-Tzung Chien, Elsevier Science
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Author: Jen-Tzung Chien ISBN: 9780128045770
Publisher: Elsevier Science Publication: October 16, 2018
Imprint: Academic Press Language: English
Author: Jen-Tzung Chien
ISBN: 9780128045770
Publisher: Elsevier Science
Publication: October 16, 2018
Imprint: Academic Press
Language: English

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.

  • Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning
  • Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning
  • Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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