  We propose a frequency domain method based on robust independent component
analysis (RICA) to address the multichannel Blind Source Separation (BSS)
problem of convolutive speech mixtures in highly reverberant environments. We
impose regularization processes to tackle the ill-conditioning problem of the
covariance matrix and to mitigate the performance degradation in the frequency
domain. We apply an algorithm to separate the source signals in adverse
conditions, i.e. high reverberation conditions when short observation signals
are available. Furthermore, we study the impact of several parameters on the
performance of separation, e.g. overlapping ratio and window type of the
frequency domain method. We also compare different techniques to solve the
frequency-domain permutation ambiguity. Through simulations and real world
experiments, we verify the superiority of the presented convolutive algorithm
among other BSS algorithms, including recursive regularized ICA (RR ICA),
independent vector analysis (IVA).
