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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
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.

More books from Elsevier Science

Cover of the book The Stimulated Brain by Jen-Tzung Chien
Cover of the book The Myth and Magic of Library Systems by Jen-Tzung Chien
Cover of the book Meeting People via WiFi and Bluetooth by Jen-Tzung Chien
Cover of the book Welding and Joining of Magnesium Alloys by Jen-Tzung Chien
Cover of the book Mechanisms and Models in Rheumatoid Arthritis by Jen-Tzung Chien
Cover of the book Computer-Managed Maintenance Systems by Jen-Tzung Chien
Cover of the book Mergers, Acquisitions, and Other Restructuring Activities by Jen-Tzung Chien
Cover of the book Air Pollution and Health by Jen-Tzung Chien
Cover of the book The Effect of Sterilization on Plastics and Elastomers by Jen-Tzung Chien
Cover of the book Ludwig's Applied Process Design for Chemical and Petrochemical Plants by Jen-Tzung Chien
Cover of the book Kinetic Energy Storage by Jen-Tzung Chien
Cover of the book Neuropsychiatric Disorders and Epigenetics by Jen-Tzung Chien
Cover of the book Medical Device Technologies by Jen-Tzung Chien
Cover of the book Cognition, Brain, and Consciousness by Jen-Tzung Chien
Cover of the book Ion Transport in Prokaryotes by Jen-Tzung Chien
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy