Open Access

Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments

EURASIP Journal on Advances in Signal Processing20062006:017021

https://doi.org/10.1155/ASP/2006/17021

Received: 23 January 2005

Accepted: 22 August 2005

Published: 7 June 2006

Abstract

Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms.

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Authors’ Affiliations

(1)
Western Australian Telecommunications Research Institute
(2)
National ICT Australia
(3)
Computer Science Laboratory, Australian National University

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Copyright

© Lehmann and Williamson 2006