Blind separation of binaural sound mixtures using SIMO-ICA with self-generator for initial filter

In this paper, we address the blind separation problem of binaural mixed signals, and propose a novel blind separation method using Single-Input-Multiple-Output-model-based independent component analysis (SIMO-ICA) with a self-generator (SG) for the initial filter. SIMO-ICA which has been proposed by the authors can separate mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Although this attractive feature of SIMO-ICA is beneficial to the binaural sound separation, SIMO-ICA has a serious drawback in its high sensitivity to the initial settings of the separation filter. In the proposed method, the SG functions as the preprocessor of SIMO-ICA, and it can provide a valid initial filter for SIMO-ICA. To evaluate its effectiveness, binaural sound separation experiments are carried out under a reverberant condition. The experimental results reveal that the separation performance of the proposed method is superior to those of conventional methods.