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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 originate from. This paper addresses real-time sound source tracking by real-time 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 beam-forming 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. The localization results are integrated to track multiple sound sources by using a particle filter in real-time. The experimental results show that particle filter based integration improved accuracy and robustness in multiple sound source tracking even when the robot's head was in rotation
Nakadai et al. (Sun,) studied this question.