High-fidelity blind separation for convolutive mixture of acoustic signals using SIMO-model-based independent component analysis

We propose a novel blind separation framework for single-input multiple-output (SIMO)-model-based acoustic signals using the extended ICA algorithm, SlMO-ICA. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The 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 SIMO-ICA can maintain the spatial qualities of each sound source. In order to evaluate its effectiveness, separation experiments are carried out under a reverberant condition. The experimental results reveal that (1) the signal separation performance of the proposed SIMO-ICA is the same as that of the conventional ICA-based method, and that (2) the spatial quality of the separated sound in SIMO-ICA is remarkably superior to that of the conventional method.