Source Separation and Machine Learning

Gain insights into the latest algorithms for source separation and machine learning techniques

Jen-Tzung Chien (Author)

9780128177969

Paperback, published 23 October 2018

384 pages
23.4 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.

Part I Fundamental Theories1. Introduction2. Model-based blind source separation3. Adaptive learning machine

Subject Areas: Electronics engineering [TJF]