I am a CCB Research Associate and apply machine learning and statistics in order to decode information about wildlife, mainly whales and elephants, from years of acoustic data. Through the processing of endless sound, we are able to understand when animals vocalize (sound detection), what species it is (sound classification), where they vocalize (sound localization) and what sounds they make (call characterization). The goal of doing all these quantitative exercises, with a clear scientific question, experimental design, and behavioral information, is to understand how animals communicate as well as monitor their presence and population. I’m also looking for new generations of sensors so that we can collect a comprehensive dataset of everything about wildlife.
Shiu, Y. et al. (2020) ‘Deep neural networks for automated detection of marine mammal species’, Scientific Report, 10(607). doi: 10.1038/s41598-020-57549-y.
Charif, R. A. et al. (2019) ‘Phenological changes in North Atlantic right whale habitat use in Massachusetts Bay’, Global Change Biology, pp. 1–12. doi: 10.1111/gcb.14867.
Root-Gutteridge, H. et al. (2018) ‘A lifetime of changing calls: North Atlantic right whales, Eubalaena glacialis, refine call production as they age’, Animal Behaviour, 137, pp. 21–34.
Guerra, M. et al.
(2016) ‘High-resolution analysis of seismic air gun impulses and their reverberant field as contributors to an acoustic environment’, in Popper, N. A. and Hawkins, A. (eds) The Effects of Noise on Aquatic Life II
. New York, NY: Springer New York, pp. 371–379. Available at: http://dx.doi.org/10.1007/978-1-4939-2981-8_44
Keen, S. C. et al. (2017) ‘Automated detection of low-frequency rumbles of forest elephants: A critical tool for their conservation’, The Journal of the Acoustical Society of America, 141. doi: 10.1121/1.4979476.
Wrege, P. H. et al. (2017) ‘Acoustic monitoring for conservation in tropical forests:examples from forest elephants’, Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12730.