Two-stage blind source separation based on ICA and binary masking for real-time robot audition system

Abstract

We newly propose a real-time two-stage blind source separation (BSS) for binaural mixed signals observed at the ears of humanoid robot, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and binary mask processing are combined. SIMO-model-based 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-model-based ICA can maintain the spatial qualities of each sound source, and this yields that binary mask processing can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results obtained with a human-like head reveal that the separation performance can be considerably improved by using the proposed method in comparison to the conventional ICA-based and binary-mask-based BSS methods.