Bund source separation based on Multi-Stage ICA combining frequency-domain ICA and time-domain ICA

We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, the conventional TDICA fails to separate source signals under heavily reverberant conditions because of the low convergence in the iterative learning of the inverse of the mixing system. On the other hand, the separation performance of the conventional FDICA under reverberant conditions also degrades significantly because the independence assumption of narrowband signals collapses when the number of subbands increases. In the proposed method, the separated signals of FDICA are regarded as the input signals for TDICA, and we can remove the residual cross-talk components of FDICA by using TDICA. The experimental results under the reverberant condition reveal that the signal-separation performance of the proposed method is superior to that of the conventional ICA-based BSS methods.