  Convolutive blind source separation (BSS) is intended to recover the unknown
components from their convolutive mixtures. Contrary to the contrast functions
used in instantaneous cases, the spatial-temporal prewhitening stage and the
para-unitary filters constraint are difficult to implement in a convolutive
context. In this paper, we propose several modifications of FastICA to
alleviate these difficulties. Our method performs the simple prewhitening step
on convolutive mixtures prior to the separation and optimizes the contrast
function under the diagonalization constraint implemented by single value
decomposition (SVD). Numerical simulations are implemented to verify the
performance of the proposed method.
