EURASIP Journal on Applied Signal Processing
Volume 2006 (2006), Article ID 17021, 9 pages
doi:10.1155/ASP/2006/17021

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

Eric A. Lehmann1 and Robert C. Williamson2,3

1Western Australian Telecommunications Research Institute, 35 Stirling Highway, Crawley 6009, WA, Australia
2National ICT Australia, Locked Bag 8001, Canberra 2601, ACT, Australia
3Computer Science Laboratory, Australian National University, Canberra 0200, ACT, Australia

Received 23 January 2005; Revised 29 May 2005; Accepted 22 August 2005

Copyright © 2006 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.