A survey of semi-blind ICA for speech separation in frequency domain

Independent component analysis (ICA) consists of recovering a set of maximally independent sources from their observed mixtures without knowledge of the source signals and the mixing parameters. It has obtained promising results in multi-channel speech separation. In practice, some prior information is available to provide additional constraints on estimation of the sources or the mixing parameters. Recent work has suggested that incorporating prior information into the estimation process, also called semi-blind ICA, can improve the potential of ICA. In this paper, we provide a brief review of existing semi-blind ICA algorithms for frequency-domain speech separation. We emphasize what prior information is utilized and how it is used. This could be helpful for developing new semi-blind speech separation algorithms in the frequency domain.