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

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 017021 (2006) Cite this article

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 and Affiliations

  1. Western Australian Telecommunications Research Institute, 35 Stirling Highway, Crawley, WA, 6009, Australia

    Eric A. Lehmann

  2. National ICT Australia, Locked Bag 8001, Canberra, ACT 2601, Australia

    Robert C. Williamson

  3. Computer Science Laboratory, Australian National University, Canberra, ACT 0200, Australia

    Robert C. Williamson

Authors
  1. Eric A. Lehmann
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  2. Robert C. Williamson
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Correspondence to Eric A. Lehmann.

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Lehmann, E.A., Williamson, R.C. Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments. EURASIP J. Adv. Signal Process. 2006, 017021 (2006). https://doi.org/10.1155/ASP/2006/17021

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

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