Jen-Tzung Chien, 'Source Separation and Machine Learning', Academic Press

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
singlechannel source separation.

Key features:
? 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, using a variety of learning
algorithms that provide solutions for the construction of BSS systems

https://www.elsevier.com/books/source-separation-and-machine-learning/chien/978-0-12-804566-4