Blind separation and deconvolution of MIMO system driven by colored inputs using SIMO-model-based ICA with information-geometric learning

We propose a new two-stage blind separation and deconvolution algorithm for multiple-input multiple-output (MIMO)- FIR system driven by colored sound sources, in which a new single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources. After SIMO-ICA, a simple blind deconvolution technique for the SIMO model can be applied even when each source signal is temporally correlated. The simulation results reveal that the proposed algorithm can successfully achieve the separation and deconvolution for a convolutive mixture of speech.