Product Description
Source Separation and Machine Learning
Gain insights into the latest algorithms for source separation and machine learning techniques
Jen-Tzung Chien (Author)
9780128177969, Elsevier Science
Paperback / softback, published 23 October 2018
384 pages
23.5 x 19 x 2.4 cm, 0.72 kg
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 and Machine Learning Chien Paperback Academic Press
Source Separation and Machine Learning
Gain insights into the latest algorithms for source separation and machine learning techniques
Jen-Tzung Chien (Author)
9780128177969, Elsevier Science
Paperback / softback, published 23 October 2018
384 pages
23.5 x 19 x 2.4 cm, 0.72 kg
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 ]
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