Whistles characterisation using artificial intelligence: responses of short-beaked common dolphins (Delphinus delphis) to a bio-inspired acoustic mitigation device

Whistles characterisation using artificial intelligence: responses of short-beaked common dolphins (Delphinus delphis) to a bio-inspired acoustic mitigation device | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Whistles characterisation using artificial intelligence: responses of short-beaked common dolphins (Delphinus delphis) to a bio-inspired acoustic mitigation device Loïc Lehnhoff, Hervé Glotin, Yves Le Gall, Hélène Peltier, Alain Pochat, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5234650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Understanding cetacean whistles is crucial for assessing their social interactions, behaviors, and responses to anthropicactivities. However, to detect and dissociate different kinds of whistles within acoustic records remains challenging. Wedeveloped an innovative semi-automatic deep learning approach (DYOC) to rapidly extract whistle contours from audiorecordings, using YOLOv8m for detection and ResNet18 for identification. Applied to 808 minutes of audio recordings of wildfree-ranging short-beaked common dolphin from the Bay of Biscay, France, DYOC enabled the annotation of 8,730 contours6 times faster than manual annotation. Their features (such as duration, frequency range, and number of inflections) werethen compared based on dolphin behavior, presence of fishing nets, and the DOLPHINFREE acoustic beacon’s influence.Beacon activation led to significant frequency shifts and lower Signal-to-Noise Ratios, while during activation and deactivationphases, whistles were longer with more inflections. A dimension reduction technique (UMAP) revealed gradients betweenarchetypal whistle shapes. This study provides the first characterisation of whistle features for a population of short-beakedcommon dolphins in the Bay of Biscay. The proposed methodological approach has the potential to be applied to the study ofwhistles across a wide range of research areas, species and applications related to animal behaviour. Physical sciences/Mathematics and computing Biological sciences/Ecology Biological sciences/Ecology/Behavioural ecology Physical sciences/Physics/Applied physics/Acoustics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 08 Nov, 2024 Editor assigned by journal 08 Nov, 2024 Editor invited by journal 23 Oct, 2024 Submission checks completed at journal 22 Oct, 2024 First submitted to journal 09 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5234650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369385560,"identity":"5a0646e1-137b-41b0-b189-76c3bf549caf","order_by":0,"name":"Loïc 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