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portada Source Separation and Machine Learning
Type
Physical Book
Publisher
Author
Year
2018
Language
English
Pages
384
Format
Paperback
ISBN13
9780128177969
Edition No.
1
Categories

Source Separation and Machine Learning

Jen-Tzung Chien (Author) · Academic Press · Paperback

Source Separation and Machine Learning - Jen-Tzung Chien

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Synopsis "Source Separation and Machine Learning "

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 LearningIncludes 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

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