High-fidelity Blind Separation of Acoustic Signals Using Simo-model-based Ica with Information-geometric Learning

We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. 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 SIMOICA 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 the signal separation performance of the proposed SIMO-ICA is the same as that of the conventional ICA-based method, and that The sound quality of the separated sound in SIMO-ICA is superior to that of the conventional method.