RT Journal Article
SR Electronic
T1 Towards a General Approach for Bat Echolocation Detection and Classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.12.14.520490
DO 10.1101/2022.12.14.520490
A1 Oisin Mac Aodha
A1 Santiago Martínez Balvanera
A1 Elise Damstra
A1 Martyn Cooke
A1 Philip Eichinski
A1 Ella Browning
A1 Michel Barataud
A1 Katherine Boughey
A1 Roger Coles
A1 Giada Giacomini
A1 M. Cristina Mac Swiney G.
A1 Martin K. Obrist
A1 Stuart Parsons
A1 Thomas Sattler
A1 Kate E. Jones
YR 2022
UL http://biorxiv.org/content/early/2022/12/16/2022.12.14.520490.abstract
AB Acoustic monitoring is an effective and scalable way to assess the health of important bioindicators like bats in the wild. However, the large amounts of resulting noisy data requires accurate tools for automatically determining the presence of different species of interest. Machine learning-based solutions offer the potential to reliably perform this task, but can require expertise in order to train and deploy.We propose BatDetect2, a novel deep learning-based pipeline for jointly detecting and classifying bat species from acoustic data. Distinct from existing deep learning-based acoustic methods, BatDetect2’s outputs are interpretable as they directly indicate at what time and frequency a predicted echolocation call occurs. BatDetect2 also makes use of surrounding temporal information in order to improve its predictions, while still remaining computationally efficient at deployment time.We present experiments on five challenging datasets, from four distinct geographical regions (UK, Mexico, Australia, and Brazil). BatDetect2 results in a mean average precision of 0.88 for a dataset containing 17 bat species from the UK. This is significantly better than the 0.71 obtained by a traditional call parameter extraction baseline method.We show that the same pipeline, without any modifications, can be applied to acoustic data from different regions with different species compositions. The data annotation, model training, and evaluation tools proposed will enable practitioners to easily develop and deploy their own models. BatDetect2 lowers the barrier to entry preventing researchers from availing of effective deep learning bat acoustic classifiers. Open source software is provided at:https://github.com/macaodha/batdetect2Competing Interest StatementThe authors have declared no competing interest.