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Bat echolocation call identification for biodiversity monitoring: a probabilistic approach

Stathopoulos, V; Zamora-Gutierrez, V; Jones, KE; Girolami, M; (2018) Bat echolocation call identification for biodiversity monitoring: a probabilistic approach. The Journal of the Royal Statistical Society, Series C (Applied Statistics) , 67 (1) pp. 165-183. 10.1111/rssc.12217. Green open access

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Abstract

Bat echolocation call identification methods are important in developing efficient cost-effective methods for large-scale bioacoustic surveys for global biodiversity monitoring and conservation planning. Such methods need to provide interpretable probabilistic predictions of species since they will be applied across many different taxa in a diverse set of applications and environments. We develop such a method using a multinomial probit likelihood with independent Gaussian process priors and study its feasibility on a data set from an on-going study of 21 species, five families and 1800 bat echolocation calls collected from Mexico, a hotspot of bat biodiversity. We propose an efficient approximate inference scheme based on the expectation propagation algorithm and observe that the overall methodology significantly improves on currently adopted approaches to bat call classification by providing an approach which can be easily generalized across different species and call types and is fully probabilistic. Implementation of this method has the potential to provide robust species identification tools for biodiversity acoustic bat monitoring programmes across a range of taxa and spatial scales.

Type: Article
Title: Bat echolocation call identification for biodiversity monitoring: a probabilistic approach
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12217
Publisher version: http://doi.org/10.1111/rssc.12217
Language: English
Additional information: Copyright © 2017 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Acoustic monitoring; Approximate Bayesian inference; Classification; Gaussian processes
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/1536162
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