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2009 | 11 | 1 |
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Detecting bat calls: an analysis of automated methods

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Long-term and large-scale acoustic surveys of bats have become possible with the increased availability of recording hardware and advances in battery and memory storage technologies. The volume of data generated in surveys necessitates automated call detection, either in real time via a triggering function or offline, yet researchers are hesitant to replace traditional hand analysis without a thorough understanding of the accuracy and costs of automated detection. We compared detection accuracy and computational cost of the underlying algorithms used in commercial detectors (a zero-crossing detector, a spectral peak detector, and a high-band energy detector) with a model-based analysis method called the links detector. We predicted that the links detector would be more accurate than the other detectors, producing a larger effective detection range, because the links detector uses more information to make detection decisions. We also predicted that the links detector would be the most computationally expensive algorithm because of the processing needed for the extra information. We quantified the performance of the detectors using a synthetic recording environment, which provided an absolute ground truth for the experiments and allowed us to measure the effective detection range of each algorithm. The zero-crossing and high-band energy detectors, the fastest, were about 40 times faster than the links detector. Most of the computational cost was attributed to the filter used to remove low-frequency noise. The links detector, the most accurate, increased effective detection range by 6-12 m compared to the other detectors depending on species. The results will allow bat researchers to better understand the costs and benefits of automated detection methods.
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  • Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada
  • Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada
  • Bass, H. E., L. C. Sutherland, and A. J. Zuckerwar. 1990. Atmospheric absorption of sound: update. Journal of the Acoustical Society of America, 88: 2019-2021.
  • Griffin, D. R. 1958. Listening in the dark: the acoustic orientation of bats and men. Comstock, Ithaca, NY, 413 pp.
  • Hooper, J. 1969. Potential use of a portable ultrasonic receiver for the field identification of flying bats. Ultrasonics, 7: 177-181.
  • Jensen, M. E., and L. A. Miller. 1997. Source levels of bat biosonar signals measured in the field using microphone arrays. In Proceedings of the 25th Gottingen Neurobiological Conference, Georg Thieme-Verlag, Stuttgart, 2: 361.
  • Lausen, C. L., and R. M. R. Barclay. 2006. Winter bat activity in the Canadian prairies. Canadian Journal of Zoology, 84: 1079-1086.
  • Miller, L. A., and A. E. Treat. 1993. Field recording of echolocation and social signals from the gleaning bat Myotis septentrionalis. Bioacoustics, 5: 67-87.
  • Minka, T. 2008. Lightspeed Matlab toolbox.
  • O’Farrell, M. J., B. W. Miller, and W. L. Gannon. 1999. Qualitative identification of free-flying bats using the Anabat detector. Journal of Mammalogy, 80: 11-23.
  • Parsons, S., and G. Jones. 2000. Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. Journal of Experimental Biology, 203: 2641-2656.
  • Skowronski, M. D., and M. B. Fenton. 2008a. Model-based automated detection of echolocation calls using the links detector. Journal of the Acoustical Society of America, 124: 328-336.
  • Skowronski, M. D., and M. B. Fenton. 2008b. Model-based detection of synthetic bat echolocation calls using an energy threshold detector for initialization. Journal of the Acoustical Society of America, 123: 2643-2650.
  • Skowronski, M. D., and M. B. Fenton. 2008c. Quantifying bat call detection performance of humans and machines. Journal of the Acoustical Society of America, 125: 513-521.
  • Skowronski, M. D., and J. G. Harris. 2006. Acoustic microchiroptera detection and classification using machine learning: lessons learned from automatic speech recognition. Journal of the Acoustical Society of America, 119: 1817-1833.
  • Williams, S. M., S. J. Wolbert, and H. P. Whidden. 2007. Evaluation of the program SCAN’R for sorting ultrasonic recordings of bat vocalizations. Proceedings of the Northeast Bat Working Group, North Branch, NJ.
  • Zhuang, Q., and R. Müller. 2007. Numerical study of the effect of the noseleaf on biosonar beamforming in a horseshoe bat. Physics Review, 76E: 051902-1-11.
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