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

Murray, John and Erwin, Harry and Wermter, Stefan (2005) A hybrid architecture using cross-correlation and recurrent neural networks for acoustic tracking in robots. In: Biomimetic neural learning for intelligent robots: intelligent systems, cognitive robotics and neuroscience. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence (3575). Springer, pp. 73-87. ISBN 3540274405

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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.

Keywords:mobile robotics, Robotics, Service Robotics., cross correlation, auditory cognition
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:8151
Deposited On:21 Mar 2013 17:21

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