Let *S* be a *n* rows, *p* columns matrix that
describes a set of *n* functions defined for
*p* values of the same variable.
A set of *m* linear combinations of these
*n* functions (*m* greater or equal to *n*)
are described by a
*m* rows, *p* columns matrix *X* so that
*X = AS* where *A* contains the mixing coefficients.

Blind Source Separation (BSS) consists in finding *S*,
the sources, and *A*, the mixing matrix,
with only *X* as data !!

Obviously, if *S* is a solution, the matrix that is obtained
by scaling its rows by any factor and/or by permuting them is
also a solution. Solving a BSS problem requires some
prior knowledge about the sources. The existing BSS algorithms
mainly differ by the nature of this prior.
I have participated to the
elaboration of two such algorithms, one named
*f*-SOBI,
derived from the Second-Order Blind Identification method,
and a really original one, named
LP-BSS

Jean-Marc Nuzillard

jm.nuzillard@univ-reims.fr

March 25^{th} 2005