Source Separation and Machine Learning

Jen Tzung Chien*

*Corresponding author for this work

Research output: Book/ReportBookpeer-review

15 Scopus citations


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.
Original languageEnglish
PublisherAcademic Press Inc.
Number of pages384
ISBN (Electronic)9780128045770
ISBN (Print)9780128177969
StatePublished - 30 Oct 2018


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