Blind separation of binaural sound mixtures using SIMO-model-based independent component analysis

High-fidelity blind audio signal separation is addressed, adopting the extended ICA algorithm, single-input multiple-output (SIMO)-model-based ICA. The SIMO-ICA consists of multiple ICA parts and a fidelity controller, and each ICA runs in parallel under fidelity control of the entire separation system. SIMO-ICA can separate the mixed signals, not into monaural source signals, but into SIMO-model-based signals from independent sources as they are at the microphones. Thus, the separated signals of the SIMO-ICA can maintain the spatial qualities of each sound source. We apply the SIMO-ICA to the problem of blind separation of mixed binaural sounds, including the effect of the head-related transfer function (HRTF). Experimental results reveal that the performance of the proposed SIMO-ICA is superior to that of the conventional ICA-based method, and the separated signals of SIMO-ICA maintain the spatial qualities of each sound source.