论文信息 - Canonical correlation analysis: a blind source separation using non-circularity

Canonical correlation analysis: a blind source separation using non-circularity

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.

J. Galy | C. Adnet

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