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.
Table of Contents
Part I Fundamental Theories 1. Introduction 2. Model-based blind source separation 3. Adaptive learning machine
Part II Advanced Studies 4. Independent component analysis 5. Nonnegative matrix factorization 6. Nonnegative tensor factorization 7. Deep neural network 8. Summary and Future Trends
