Title :
Canonical correlation analysis: a blind source separation using non-circularity
Author :
Galy, Jérôme ; Adnet, Claude
Author_Institution :
LIRMM, CNRS, Montpellier, France
Abstract :
Blind source separation is now a well known problem. When a priori information about the propagation or the geometry of the array are not available, the model can be generalized to a blind source separation model. It supposes the statistical independence of the sources and their non-gaussianity. We focus on an algorithm called canonical correlation analysis, based on the use of second order statistics
Keywords :
correlation theory; statistics; blind source separation; canonical correlation analysis; geometry; noncircularity; second order statistics; statistical independence; Algorithm design and analysis; Blind source separation; Higher order statistics; Integrated circuit noise; Random variables; Signal analysis; Signal processing; Source separation; Statistical analysis; Telecommunication computing;
Conference_Titel :
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6278-0
DOI :
10.1109/NNSP.2000.889439
