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Bat echolocation call identification for biodiversity monitoring: a probabilistic approach

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Title: Bat echolocation call identification for biodiversity monitoring: a probabilistic approach
Authors: Stathopoulos, V
Zamora-Gutierrez, V
Jones, KE
Girolami, M
Item Type: Journal Article
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.
Issue Date: 1-Jan-2018
Date of Acceptance: 1-Feb-2017
URI: http://hdl.handle.net/10044/1/66032
DOI: https://dx.doi.org/10.1111/rssc.12217
ISSN: 0035-9254
Publisher: Wiley
Start Page: 165
End Page: 183
Journal / Book Title: Journal of the Royal Statistical Society Series C: Applied Statistics
Volume: 67
Issue: 1
Copyright Statement: © 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 (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Acoustic monitoring
Approximate Bayesian inference
Classification
Gaussian processes
GAUSSIAN PROCESS CLASSIFICATION
MONTE-CARLO
ACOUSTIC IDENTIFICATION
RECOGNITION
REGRESSION
ALGORITHM
EVOLUTION
BINARY
0104 Statistics
Publication Status: Published
Open Access location: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssc.12217
Online Publication Date: 2017-02-20
Appears in Collections:Mathematics
Statistics



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