Simple statistical models for blind source separation
Plenary Talk 2
JeanFrançois Cardoso (GETTélécom Paris, CNRS)
September 13, 2006 at 09H00
Abstract
Source separation consists in processing multichannel signals in
order to extract from them (hypothetical) underlying elementary
components (a typical application domain is the separation of sound
sources using several microphones). Blind source separation (BSS)
is the art of doing so by resorting only to statistical properties of
the sources (mutual independence, non stationarity, sparseness, ...),
i.e. without modeling propagation, transduction, etc. This talk
presents the statistical ideas behind BSS and shows how simple source
models, if properly exploited, are sufficient for "blind processing".
