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Bat echolocation call identification for biodiversity monitoring : a probabilistic approach
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Stathopoulos, Vassilios, Zamora-Gutierrez, Veronica, Jones, Kate E. and Girolami, Mark (2017) Bat echolocation call identification for biodiversity monitoring : a probabilistic approach. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67 (1). pp. 165-183. doi:10.1111/rssc.12217 ISSN 0035-9254.
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Official URL: https://doi.org/10.1111/rssc.12217
Abstract
Summary. 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.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
SWORD Depositor: | Library Publications Router | ||||
Journal or Publication Title: | Journal of the Royal Statistical Society: Series C (Applied Statistics) | ||||
Publisher: | Wiley-Blackwell | ||||
ISSN: | 0035-9254 | ||||
Official Date: | 20 February 2017 | ||||
Dates: |
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Volume: | 67 | ||||
Number: | 1 | ||||
Page Range: | pp. 165-183 | ||||
DOI: | 10.1111/rssc.12217 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
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