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

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.

References

  1. 1.

    Dvorkind TG, Gannot S: Speaker localization exploiting spatial-temporal information. Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC '03), September 2003, Kyoto, Japan 295–298.

    Google Scholar 

  2. 2.

    Vermaak J, Blake A: Nonlinear filtering for speaker tracking in noisy and reverberant environments. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USA 5: 3021–3024.

    Google Scholar 

  3. 3.

    Ward DB, Lehmann EA, Williamson RC: Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Transactions on Speech and Audio Processing 2003, 11(6):826–836. 10.1109/TSA.2003.818112

    Article  Google Scholar 

  4. 4.

    Ward DB, Williamson RC: Particle filter beamforming for acoustic source localization in a reverberant environment. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 2: 1777–1780.

    Google Scholar 

  5. 5.

    DiBiase J, Silverman H, Brandstein M: Robust localization in reverberant rooms. In Microphone Arrays: Signal Processing Techniques and Applications. Edited by: Brandstein M, Ward DB. Springer, Berlin, Germany; 2001:157–180.

    Google Scholar 

  6. 6.

    Knapp C, Carter G: The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing 1976, 24(4):320–327. 10.1109/TASSP.1976.1162830

    Article  Google Scholar 

  7. 7.

    Benesty J: Adaptive eigenvalue decomposition algorithm for passive acoustic source localization. Journal of the Acoustical Society of America 2000, 107(1):384–391. 10.1121/1.428310

    Article  Google Scholar 

  8. 8.

    Ward DB: Nonlinear filtering of the generalized cross-correlation function for source localization. Proceedings of IEE Workshop on Nonlinear and Non-Gaussian Signal Processing, July 2002, Peebles Hydro, UK

    Google Scholar 

  9. 9.

    Gordon NJ, Salmond DJ, Smith AFM: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F Radar and Signal Processing 1993, 140(2):107–113. 10.1049/ip-f-2.1993.0015

    Article  Google Scholar 

  10. 10.

    Doucet A, Godsill S, Andrieu C: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 2000, 10(3):197–208. 10.1023/A:1008935410038

    Article  Google Scholar 

  11. 11.

    Arulampalam MS, Maskell S, Gordon N, Clapp T: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002, 50(2):174–188. 10.1109/78.978374

    Article  Google Scholar 

  12. 12.

    Doucet A, de Freitas N, Gordon N (Eds): Sequential Monte Carlo Methods in Practice. Springer, New York, NY, USA; 2001.

    Google Scholar 

  13. 13.

    Vermaak J, Gangnet M, Blake A, Pérez P: Sequential Monte Carlo fusion of sound and vision for speaker tracking. Proceedings of 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 1: 741–746.

    Google Scholar 

  14. 14.

    Isard M, Blake A: ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. Proceedings of 5th European Conference on Computer Vision (ECCV '98), June 1998, Freiburg, Germany 1: 893–908.

    Google Scholar 

  15. 15.

    Lehmann EA, Ward DB, Williamson RC: Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 177–180.

    Google Scholar 

  16. 16.

    Brandstein M, Ward DB (Eds): Microphone Arrays: Techniques and Applications. Springer, Berlin, Germany; 2001.

    Google Scholar 

  17. 17.

    Ristic B, Arulampalam S, Gordon N: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Boston, Mass, USA; 2004.

    Google Scholar 

  18. 18.

    Allen J, Berkley D: Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America 1979, 65(4):943–950. 10.1121/1.382599

    Article  Google Scholar 

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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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Keywords

  • Particle Filter
  • Importance Sampling
  • Audio Recording
  • Filter Design
  • Tracking Accuracy