Time-domain fast fixed-point algorithms for convolutive ICA

this paper describes our FastICA-like algorithm for convolutive mixtures, that we derived by extending the classical FastICA method which is intended for linear instantaneous mixtures.International audienceThis letter presents new blind separation methods for moving average (MA) convolutive mixtures of independent MA processes. They consist of time-domain extensions of the FastICA algorithms developed by Hyvarinen and Oja for instantaneous mixtures. They perform a convolutive sphering in order to use parameter- free fast fixed-point algorithms associated with kurtotic or negentropic non-Gaussianity criteria for estimating the source innovation processes. We prove the relevance of this approach by mapping the mixtures into linear instantaneous ones. Test results are presented for artificial colored signals and speech signals