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  • Research Article
  • Open Access

An Automated Acoustic System to Monitor and Classify Birds

  • 1Email author,
  • 2,
  • 1,
  • 2,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 3 and
  • 3
EURASIP Journal on Advances in Signal Processing20062006:096706

  • Received: 4 May 2005
  • Accepted: 11 October 2005
  • Published:


This paper presents a novel bird monitoring and recognition system in noisy environments. The project objective is to avoid bird strikes to aircraft. First, a cost-effective microphone dish concept (microphone array with many concentric rings) is presented that can provide directional and accurate acquisition of bird sounds and can simultaneously pick up bird sounds from different directions. Second, direction-of-arrival (DOA) and beamforming algorithms have been developed for the circular array. Third, an efficient recognition algorithm is proposed which uses Gaussian mixture models (GMMs). The overall system is suitable for monitoring and recognition for a large number of birds. Fourth, a hardware prototype has been built and initial experiments demonstrated that the array can acquire and classify birds accurately.


  • Information Technology
  • Mixture Model
  • Quantum Information
  • Initial Experiment
  • Recognition System

Authors’ Affiliations

Intelligent Automation, Inc., 15400 Calhoun Drive, Suite 400, Rockville, MD 20855, USA
Department of Electrical and Computer Engineering, University of Missouri-Columbia, 349 Engineering Building West, Columbia, MO 65211, USA
National Institute of Standards and Technology, Building 225, Room A216, Gaithersburg, MD 20899, USA


  1. Federal Aviation Agency : Wildlife strikes to civil aircraft in the United States. 2001.Google Scholar
  2. Kwan C, Ho KC, Mei G, et al.: An automated acoustic system to monitor and classify birds. Proceedings of 5th Annual Meeting of Bird Strike Committee-USA/Canada (Bird Strike '03), August 2003, Toronto, CanadaGoogle Scholar
  3. Buckley KM, Griffiths L: Broad-band signal-subspace spatial-spectrum (BASS-ALE) estimation. IEEE Transactions on Acoustics, Speech, and Signal Processing 1988, 36(7):953–964. 10.1109/29.1617View ArticleGoogle Scholar
  4. Schmidt R: Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation 1986, 34(3):276–280. 10.1109/TAP.1986.1143830View ArticleGoogle Scholar
  5. Wang H, Kaveh M: Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources. IEEE Transactions on Acoustics, Speech, and Signal Processing 1985, 33(4):823–831. 10.1109/TASSP.1985.1164667View ArticleGoogle Scholar
  6. Kwan C, Ho KC, Mei G, et al.: Phase 1 Progress Report 3 to the Air Force. In Internal Research Report. Intelligent Automation, Poway, Calif, USA; 2003.Google Scholar
  7. Li Y, Ho KC, Kwan C: 3-D array pattern synthesis with frequency invariant property for concentric ring array. to appear in IEEE Transactions on Signal ProcessingGoogle Scholar
  8. Kreyszig E: Advanced Engineering Mathematics. 7th edition. John Wiley & Sons, New York, NY, USA; 1993.MATHGoogle Scholar
  9. Stearns CO, Stewart AC: An investigation of concentric ring antennas with low sidelobes. IEEE Transactions on Antennas and Propagation 1965, 13(6):856–863. 10.1109/TAP.1965.1138544View ArticleGoogle Scholar
  10. NIST web site,
  11. NIST web site,
  12. Reynolds DA, Rose RC: Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing 1995, 3(1):72–83. 10.1109/89.365379View ArticleGoogle Scholar


© C. Kwan et al. 2006

This article is published under license to BioMed Central Ltd. 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.