COMPLETE TRAINING ANALYSIS OF FEEDBACK ARCHITECTURE NETWORKS THAT PERFORM BLIND SOURCE SEPARATION AND DECONVOLUTION
Nikos A. Kanlis, Jonathan Z. Simon, and Shihab A. Shamma
In this paper we address the diĘcult problem of separating
multiple speakers in a real-world situation, were the record-
ings are not just instantaneous mixtures, but rather mix-
tures of ltered versions of the sources. The enhancement
over the approaches already presented by other researchers
is that our model allows direct-path, zero-delayed versions
of all the sources to be present in each one of the mixtures (a
diĘcult approach because it introduces recursiveness in the
model, but a closer to the reality one). The update rules are
all derived in matrix form (suitable for computing environ-
ments, e.g Matlab), with special attention to the diagonals
of those matrices, in order to avoid \temporal whitening"
at the output. Extending those update rules to ones based
on \natural" gradient is also addressed.