Extraction of Specific Signals with Temporal Structure

[Allan Kardec Barros]

Apologies if you receive multiple copies of this message.

Dear Everyone,

    I would like to announce the following paper, recently published in
Neural Computation. For those familiar with ICA, the difference in this
algorithm is basically that, given some simple assumptions, we prove
that the permutation problem can be avoided, while the algorithm is
quite simple and based on second order statistics, which does not
require that at most one signal to be Gaussian.

    Please feel free to mail me requesting either PS or PDF copies of
our work.

    Best Regards,

ak.

TITLE: Extraction of Specific Signals with Temporal Structure.

AUTORS: A. K. Barros and A. Cichocki.

ABSTRACT:
In  this  work we  develop a very simple batch learning algorithm  for
semi-blind extraction  of a desired source signal with temporal
structure from    linear  mixtures. Although we  use the concept of
sequential blind extraction of sources and  independent component
analysis (ICA),  we do not  carry out the  extraction in  a completely
blind manner neither we assume that sources are   statistically
independent.  In fact,  we show that the  {\it a  priori} information
about the auto-correlation function  of primary sources can  be used to
extract the desired signals (sources of interest) from their linear
mixtures.   Extensive computer simulations and real data application
experiments  confirm the validity and high performance of the proposed
algorithm.