On optimal source separation based on second and fourth order cumulants

Authors:
J.-F. Cardoso;S. Bose;B. Friedlander
Affiliations:
-;-;-
Venue:
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Year:
1996

Citing 0
Cited 2

High-order contrasts for independent component analysis

Neural Computation
A cumulant-based method for the direct estimation of the spatial Wiener filter

Digital Signal Processing

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Abstract

This paper addresses performance issues in the source separation problem. By drawing on the theory of optimal statistic matching, we derive new contrast functions which are optimal among those involving a given set of cumulants. In low noise, the optimal combination of a particular set of cumulants are shown to be parameter independent and can be pre-computed. We give specific examples in closed form for several choices of 2nd and 4th order cumulants. The resulting performance is investigated as a function of the SNR for non-Gaussian source signals and further compared to suboptimal approaches.