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An Empirical Study of Collaborative Acoustic Source Localization

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Abstract

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement both two-dimensional (2D) and three-dimensional (3D) AML-based source localization algorithms. The 2D algorithm is used to localize marmot alarm-calls of marmots on the meadow ground. The 3D algorithm is used to localize the song of Acorn Woodpecker and Mexican Antthrush birds situated above the ground. We assess the performance of these techniques based on the results from four field experiments: two controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal for 2D and 3D analysis, an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations, and a controlled test of localization experiment using pre-recorded source to enable careful ground truth measurements. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat using a platform that is practical to deploy.

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References

  1. Bradbury, J., & Vehrencamp, S. (1998). Principles of animal communication. Sunderland: Sinauer.

    Google Scholar 

  2. McGregor, P., Peake, T., & Gilbert, G. (2000). Communication behavior and conservation. In L. Gosling, & W. Sutherland (Eds.), Behaviour and conservation (pp. 261–280). Cambridge: Cambridge University Press.

    Google Scholar 

  3. Blumstein, D. (2007). The evolution of alarm communication in rodents: Structure, function, and the puzzle of apparently altruistic calling in rodents. In J. Wolff, & P. Sherman (Eds.), Rodent societies. Chicago: U. Chicago Press.

    Google Scholar 

  4. Hall, M. L. (2004). A review of hypotheses for the functions of avian duetting. Behavioral Ecology and Sociobiology, 55, 415–430.

    Article  Google Scholar 

  5. Blumstein, D. T., & Armitage, K. B. (1997). Alarm calling in yellow-bellied marmots: The meaning of situationally-specific alarm calls. Animal Behavior, 53, 143–171.

    Article  Google Scholar 

  6. Blumstein, D. T., & Munos, O. (2005). Individual, age and sex-specific information is contained in yellow-bellied marmot alarm calls. Animal Behavior, 69, 353–361.

    Article  Google Scholar 

  7. Blumstein, D. T., & Daniel, J. C. (2005). Yellow-bellied marmot discriminate between the alarm calls of individuals and are more responsive to the calls from juveniles. Animal Behavior, 8, 1257–1265.

    Google Scholar 

  8. Vilches, E., Escobar, I., Vallejo, E., & Taylor, C. (2006). Data mining applied to acoustic bird species recognition. International Conference on Pattern Recognition, 3, 400–403.

    Google Scholar 

  9. Trifa, V., Girod, L., Collier, T., Blumstein, D. T., & Taylor, C. E. (2007). Automated wildlife monitoring using self-configuring sensor networks deployed in natural habitats. In The 12th international symposium on artificial life and robotics (AROB).

  10. Girod, L., Lukac, M., Trifa, V., & Estrin, D. (2006). The design and implementation of a self-calibrating distributed acoustic sensing platform. In ACM SenSys. Boulder, CO.

  11. Wang, H., et al. (2005). Acoustic sensor networks for woodpecker localization. In SPIE conference on advanced signal processing algorithms, architectures and implementations, 5910, 591009.1–591009.12.

  12. Girod, L. (2005). A self-calibrating system of distributed acoustic arrays. Ph.D. thesis, Univerity of Caliornia at Los Angeles.

  13. Elson, J., Girod, L., & Estrin, D. (2002). A wireless time-synchronized COTS sensor platform, part i: System architecture. In IEEE CAS workshop on wireless communications and networking.

  14. Girod, L., et al. (2004). Emstar: A software environment for developing and deploying wireless sensor networks. In Proceedings of the 2004 USENIX technical conference. USENIX Association, Boston, MA.

    Google Scholar 

  15. Trifa, V. (2006). A framework for bird songs detection, recognition and localization using acoustic sensor networks. Master’s thesis, École Polytechnique Fédérale de Lausanne.

  16. Chen, J., Yao, K., & Hudson, R. (2002). Maximum-likelihood source localization and unknown source localization estimation for wideband signals in the near-field. IEEE Transactions on Signal Processing, 8, 1843–1854.

    Article  Google Scholar 

  17. Kay, S. (1993). Fundamentals of statistical signal processing: Estimation theory. New Jersey: Prentice-Hall.

    MATH  Google Scholar 

  18. Baysal, U., & R.M. (2003). On the geometry of isotropic arrays. IEEE Transactions on Signal Processing, 51(6), 1469–1478.

    Article  MathSciNet  Google Scholar 

  19. Asgari, S., Ali, A., Collier, T., Yao, Y., Hudson, R., Yao, K., et al. (2007). Theoretical and experimental study of doa estimation using aml algorithm for an isotropic and non-isotropic 3d array. In SPIE conference on advanced signal processing algorithms, architectures, and implementations. SPIE, (vol. 6697, pp. 66970I-1–66970I-12).

  20. Chen, C. E., Wang, H., Ali, A. M., Lorenzelli, F., Hudson, R. E., & Yao, K. (2006). Particle filtering approach to localization and tracking of a moving acoustic source in a reverberant room. In IEEE ICASSP06 .

  21. Chen, C. E., Ali, A. M., Wang, H., Asgari, S., Park, H., Hudson, R. E., et al. (2006). Design and testing of robust acoustic arrays for localization and enhancement of several bird sources. In Symposium on information processing in sensor networks (IPSN06) (pp. 268–275). ACM Press.

  22. Chen, C. E., Lorenzelli, F., Hudson, R. E., & Yao, K. (2008). Maximum likelihood DOA estimation of multiple wideband sources in the presence of nonuniform sensor noise. EURASIP Journal on Advances in Signal Processing, 2008, 12. doi:10.1155/2008/835079.

    Google Scholar 

  23. Girod, L., Jamieson, K., Mei, Y., Newton, R., Rost, S., Thiagarajan, A., et al. (2007). The case for WaveScope: A signal-oriented data stream management system (position paper). In Proceedings of third biennial conference on innovative data systems research (CIDR07) (pp. 397–406).

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Acknowledgements

The authors would like to thank Chiao-En Chen, Wei-Ho Chung, and Yuan Yao for assistance in data collections at UCLA, Vlad Trifa and Martin Lukac for providing feedbacks on a draft of this paper, and RMBL for hosting us during the experiment. The authors would also like to thank the reviewers for their time and constructive suggestions. This work is partially supported by National Science Foundation (NSF) Center of Embedded and Network Sensing program under Cooperative Agreement CCR-012, NSF grant EF-0410438, University of California Discovery grant sponsored by ST Microelectronics, a UCLA faculty research grant to DTB, and the MIT WaveScope project (NSF).

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Authors and Affiliations

  1. University of California-Los Angeles, Los Angeles, USA

    Andreas Mantik Ali, Shadnaz Asgari, Travis C. Collier, Ralph E. Hudson, Kung Yao, Charles E. Taylor & Daniel T. Blumstein

  2. Coventry University, Coventry, UK

    Michael Allen

  3. Massachusetts Institute of Technology, Cambridge, USA

    Lewis Girod

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  1. Andreas Mantik Ali
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  2. Shadnaz Asgari
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  3. Travis C. Collier
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  4. Michael Allen
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  5. Lewis Girod
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  6. Ralph E. Hudson
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  7. Kung Yao
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  8. Charles E. Taylor
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  9. Daniel T. Blumstein
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Correspondence to Andreas Mantik Ali.

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Ali, A.M., Asgari, S., Collier, T.C. et al. An Empirical Study of Collaborative Acoustic Source Localization. J Sign Process Syst Sign Image Video Technol 57, 415–436 (2009). https://doi.org/10.1007/s11265-008-0310-7

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  • DOI: https://doi.org/10.1007/s11265-008-0310-7

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