Introduction to Blind Signal Processing: Problems and Applications
Andrzej S Cichocki, Шун-ичи Амари · 2002
Blind Signal Processing (BSP) is now one of the hottest and exciting topics in the fields of neural computation, advanced statistics, and signal processing with solid theoretical foundations and many potential applications. In fact, BSP has become a very important topic of research and development in many areas, especially biomedical engineering, medical imaging, speech enhancement, remote sensing, communication systems, exploration seismology, geophysics, econometrics, data mining, neural networks, etc. The blind signal processing techniques principally do not use any training data and do not assume a priori knowledge about parameters of convolutive filtering and mixing systems. BSP includes three major areas: Blind Signal Separation and Extraction (BSS/BSE), Independent Component Analysis (ICA), and Multichannel Blind Deconvolution (MBD) and Equalization, which are the main subjects of the book. In this chapter are formulated fundamental problems of the BSP, important definitions and descriptions of basic mathematical and physical models. Moreover, several potential and promising applications are reviewed.