Source separation and machine learning (pdf)

$40.00

Author Chien, Jen-Tzung
Edition 1
Edition Year 2019
Format PDF
ISBN 9780128045664
Language English
Number Of Pages 362
Publisher Elsevier,Academic Press

Description

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 systemsSource 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.

Additional information

Author

Chien, Jen-Tzung

Edition

1

Edition Year

2019

Format

PDF

ISBN

9780128045664

Language

English

Number Of Pages

362

Publisher

Elsevier,Academic Press

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