Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks

Murray, John C. and Erwin, Harry R. and Wermter, Stefan (2009) Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks. Neural Networks, 22 (2). pp. 173-189. ISSN 0893-6080

Full content URL: http://dx.doi.org/10.1016/j.neunet.2009.01.013

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Item Type:Article
Item Status:Live Archive

Abstract

In this paper we present a sound-source model for localising and tracking an acoustic source of interest along the azimuth plane in acoustically cluttered environments, for a mobile service robot. The model we present is a hybrid architecture using cross-correlation and recurrent neural networks to develop a robotic model accurate and robust enough to perform within an acoustically cluttered environment. This model has been developed with considerations of both processing power and physical robot size, allowing for this model to be deployed on to a wide variety of robotic systems where power consumption and size is a limitation. The development of the system we present has its inspiration taken from the central auditory system (CAS) of the mammalian brain. In this paper we describe experimental results of the proposed model including the experimental methodology for testing sound-source localisation systems. The results of the system are shown in both restricted test environments and in real-world conditions. This paper shows how a hybrid architecture using band pass filtering, cross-correlation and recurrent neural networks can be used to develop a robust, accurate and fast sound-source localisation model for a mobile robot.

Keywords:Robotics, Human–robot interaction, Sound-source localisation, Cross-correlation, Recurrent neural networks
Subjects:G Mathematical and Computer Sciences > G440 Human-computer Interaction
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:9703
Deposited On:03 Jun 2013 12:29

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