Abstract
Real-time and robust sound source tracking is an important function for a robot operating in a daily environment, because the robot should recognize where a sound event such as speech, music and other environmental sounds originates from. This paper addresses real-time sound source tracking by spatial integration of an in-room microphone array (IRMA) and a robot-embedded microphone array (REMA) . The IRMA system consists of 64 ch microphones attached to the walls. It localizes multiple sound sources based on weighted delay-and-sum beamforming on a 2D plane. The REMA system localizes multiple sound sources in azimuth using eight microphones attached to a robot's head on a rotational table. A particle filter integrates their localization results to track multiple sound sources. The experimental results show that particle filter based integration improved accuracy and robustness of sound source tracking even when the robot's head was in rotation.
