Read Source Separation and Machine Learning

Source Separation and Machine Learning

Jen-Tzung Chien

4.9/5 (29393 ratings)

Description: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 learningPresents 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 systemsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Source Separation and Machine Learning. To get started finding Source Separation and Machine Learning, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Format: PDF, EPUB & Kindle Edition

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ISBN: 0128045779

Description: 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 learningPresents 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 systemsWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Source Separation and Machine Learning. To get started finding Source Separation and Machine Learning, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Source Separation and Machine Learning

Source Separation and Machine Learning

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