Source Separation and Machine Learning
  • Release Date : 01 November 2018
  • Publisher : Academic Press
  • Genre : Technology & Engineering
  • Pages : 384 pages
  • ISBN 13 : 9780128045770
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Download or read book entitled Source Separation and Machine Learning by author: Jen-Tzung Chien which was release on 01 November 2018 and published by Academic Press with total page 384 pages . This book available in PDF, EPUB and Kindle Format. 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