** Author: ** Konstantinos I. Diamantaras
(Aristotelian University of Thessaloniki)

** Abstract: **
The blind separation of sources is usually based on some independence
assumption about the sources and higher-order statistics are used to
achieve separation. Recently it has been shown that second-order
statistics are also capable of separating sources provided that the
source signals are pairwise uncorrelated and are not white in the time
domain. If for example, the original sources are continuous-time,
bandlimited signals non-whiteness in time can be achieved by
oversampling. In this talk I propose a modification of a Hebbian type
rule originally proposed for computing the cross-correlation SVD of a
pair of signals and we show how this rule can be applied in the adaptive
blind separation of uncorrelated sources. The modified model turns into
a time-delay autoassociator of the input signal and closely resembles
Oja's single-unit rule for PCA. There are three major advantages in
unsing the second order approach: (a) the strong independence assumption
about the source signals is reduced to the weak uncorrelatedness
assumption, (b) the constraint that at most one signal can be Gaussian
is removed, and (c) the Higher-order statistical optimization methods
are replaced with simpler and better understood second order methods.