Blind source separation: theory and applications

A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies The book presents an overview of Blind Source Separation, a relatively new signal processing method. Due to the multidisciplinary nature of the subject, the book has been written so as to appeal to an audience from very different backgrounds. Basic mathematical skills (e.g. on matrix algebra and foundations of probability theory) are essential in order to understand the algorithms, although the book is written in an introductory, accessible sty.

PART I. Theory basics of BSS -- Mathematical foundation of blind source separation -- General model and classical algorithm for BSS -- Evaluation criteria for the bss algorithm -- PART II. Independent component analysis -- Independent component analysis -- Fast independent component analysis and its application -- Maximum likelihood independent component analysis and its application -- Overcomplete independent component analysis algorithms and applications -- Kernel independent component analysis -- Non-negative independent component analysis and its application -- Constraint independent component analysis algorithms and applications -- Optimized independent component analysis algorithms and applications -- Supervised learning independent component analysis algorithms and applications -- PART III. Advances and applications of BSS -- Non-negative matrix factorization algorithms and applications -- Sparse component analysis and applications -- Glossary.