CONVOLUTIVE DECORRELATION PROCEDURES FOR BLIND SOURCE SEPARATION
R. Vollgraf 1 , M. Stetter 1 , and K. Obermayer 1
D10587 Berlin, Germany; email: email@example.com
Convolutive decorrelation algorithms form a class of
powerful algorithms for blind source separation. In
contrast to ICA, they are based on vanishing second
order cross correlation functions between sources. We
provide an analyze an unifying approach for convolu
tive decorrelation procedures. The convolutive decor
relation procedures impose the problem of simultane
ously diagonalizing a number of covariance matrices.
We examine different cost functions for simultaneous
diagonalization with respect to the demixing matrix.
It turns out, that best performance is achieved for a
cost function, that takes the squared sum of the off di
agonal elements after the diagonal elements were nor
malized to unity. We then provide criteria for convolu
tion kernels, that are optimal for noise robustness and
which can guarantee positive definite covariance matri
ces, which are important for reliable convergence.