Blind sound scene decomposition for robot audition using SIMO-model-based ICA

Abstract

In this paper, we address a blind decomposition problem of binaural mixed signals observed at the ears of humanoid robot, and we introduce a novel blind signal decomposition algorithm using single-input multiple-output-model-based ICA (SIMO-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 in the robot ear. Thus, the separated signals of SIMO-ICA can maintain the spatial qualities of each sound source, i.e., they represent the decomposed sound scenes. Obviously the attractive feature of SIMO-ICA is highly applicable to not only speech recognition but also, e.g., humanoid-robot-based auditory tele-existence technology. The experimental results reveal that the spatial quality of the separated sound in SIMO-ICA is remarkably superior to that of the conventional method, particularly for the fidelity of the sound reproduction.