Source Separation and Machine Learning

Description

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.

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 (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems Readership

Graduate students and researchers in electrical engineering and computer science who are working on blind source separation problems using machine learning; Practitioners using Blind Source Separation systems

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