A hybrid architecture using cross-correlation and recurrent neural networks for acoustic tracking in robots

Abstract

Audition is one of our most important modalities and is widely used to communicate and sense the environment around us. We present an auditory robotic system capable of computing the angle of incidence (azimuth) of a sound source on the horizontal plane. The system is based on some principles drawn from the mammalian auditory system and using a recurrent neural network (RNN) is able to dynamically track a sound source as it changes azimuthally within the environment. The RNN is used to enable fast tracking responses to the overall system. The development of a hybrid system incorporating cross-correlation and recurrent neural networks is shown to be an effective mechanism for the control of a robot tracking sound sources azimuthally. © 2005 Springer-Verlag Berlin/Heidelberg.