Evaluation of blind separation and deconvolution for binaural-sound mixtures using SIMO-model-based ICA

In this paper, blind separation and deconvolution (BSD) problem with binaural-sound mixtures is addressed. We have proposed two-stage blind separation and deconvolution algorithm, which consists of Single-Input Multiple-Output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering. In the previous report, we carried out simulations in the artificial mixing system and only showed that the proposed BSD can work theoretically. In order to evaluate the proposed method in more actual situations, we carried out BSD experiments assuming that speech sources are convolved with head related transfer functions (HRTFs). The simulation results reveal that the proposed BSD method can be effective in the separation and deconvolution even with binaural-sound mixtures.