Source Separation and Machine Learning (Paperback)

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.

General

Imprint:	Academic Press Inc
Country of origin:	United States
Release date:	October 2018
First published:	2019
Authors:	Jen-Tzung Chien
Dimensions:	235 x 191 x 21mm (L x W x T)
Format:	Paperback
Pages:	384
ISBN-13:	978-0-12-817796-9
Categories:	Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	0-12-817796-9
Barcode:	9780128177969

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