Particle filter design using importance sampling for acoustic source localisation and tracking in reverberant environments

Authors:
Eric A. Lehmann;Robert C. Williamson
Affiliations:
Western Australian Telecommunications Research Institute, Crawley, WA;National ICT Australia, Locked Bag, Canberra, ACT, Australia and Computer Science Laboratory, Australian National University, Canberra, ACT, Australia
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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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.