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Monitoring Risso's Dolphins in the Northeast Atlantic: A Deep Learning Approach to Classify Echolocation Click Detections

Lookup NU author(s): Dr Matthew Sharpe

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Author(s): Webber T, Risch D, Hastie G, Sinclair R, Beck S, Berggren P, Boisseau O, Dyke K, Hartny-Mills L, Lacey C, Quer S, Sharpe M, Walters A, Young KF, van Geel N

Publication type: Article

Publication status: Published

Journal: Marine Mammal Science

Year: 2026

Volume: 42

Issue: 2

Print publication date: 20/04/2026

Online publication date: 20/04/2026

Acceptance date: 13/04/2026

Date deposited: 06/05/2026

ISSN (print): 0824-0469

ISSN (electronic): 1748-7692

Publisher: John Wiley and Sons Inc

URL: https://doi.org/10.1111/mms.70180

DOI: 10.1111/mms.70180

Data Access Statement: The best performing classifier developed here is openly accessible. The models can be run on both CPUs and CUDA- enabled GPUs, either in dependently or directly within PAMGuard. Instructions for use and re training are available here: github.com/tomwebber96/EcholocationClassification. The classifier is also provided in a Hierarchical Data Format (HDF5) which allows additional training data to be added to the existing model if required. The model architecture can also be easily extracted should a user wish to train the model from scratch and/or change the model architecture to increase performance in a new use case.

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