Bioacoustic AI for wildlife protection

HORIZON.1.2HORIZON-TMA-MSCA-DNID: 101116715
EC Contribution
€23,895
Consortium Size
20 orgs
Summary

The biodiversity crisis is coming into sharp focus. The BioacAI network will establish a vital modern evidence source for wildlife protection, by bringing the promise of AI-powered acoustic monitoring to fruition.Biodiversity loss is ranked as one of the top 5 global risks, both in impact and likelihood (World Economic Forum 2020, 2021, 2022). Continental-scale assessments warn of population declines in major taxa such as birds and insects. Yet data for monitoring biodiversity change remain incomplete - spatially, temporally and taxonomically. To stabilise biodiversity trends and assess ecosystem restoration interventions, it is crucial to monitor the world's wildlife better, faster, and to provide rapid intelligence that can enable us to manage these risks.Sound recording is a cheap, rapid and powerful way to monitor many key animal species, and modern machine learning can radically improve its scale and precision. However, there are two barriers: AI-enhanced tools for bioacoustic monitoring are at a low readiness level, with few end-to-end solutions available

Consortium (20)

Project Results (11)

Source: CORDIS, the EU research results database.

Publications (9)
Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists
Biological Reviews· 2025DOI
Arik Kershenbaum, Çağlar Akçay, Lakshmi Babu‐Saheer, Alex Barnhill, Paul Best, Jules Cauzinille, Dena Clink, Angela Dassow, Emmanuel Dufourq, Jonathan Growcott, Andrew Markham, Barbara Marti‐Domken, Ricard Marxer, Jen Muir, Sam Reynolds, Holly Root‐Gutteridge, Sougata Sadhukhan, Loretta Schindler, Bethany R. Smith, Dan Stowell, Claudia A.F. Wascher, Jacob C. Dunn
Deep Localization Refinement for Bat Abundance Estimation
Audio Engineering Society's (AES) International Conference on Artificial Intelligence and Machine Learning for Audio (AIMLA)· 2025
Roberto Alessandri, Lia Gilmour, Jin Jack, Alejandro Osses, Dan Stowell
Direction-of-Arrival Data Association for Wildlife Acoustic Localization
The 33rd European Signal Processing Conference (EUSIPCO 2025)· 2025
Manuel Alejandro Jaramillo Rodriguez, Randall Ali, Toon van Waterschoot
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
The 33rd European Signal Processing Conference (EUSIPCO 2025)· 2025DOI
Shiqi Zhang, Tuomas Virtanen
Overview of LifeCLEF 2025: Challenges on Species Presence Prediction and Identification, and Individual Animal Identification
Lecture Notes in Computer Science, Experimental IR Meets Multilinguality, Multimodality, and Interaction· 2025DOI
Lukáš Picek, Stefan Kahl, Hervé Goëau, Lukáš Adam, Théo Larcher, Cesar Leblanc, Maximilien Servajean, Klára Janoušková, Jiří Matas, Vojtěch Čermák, Kostas Papafitsoros, Robert Planqué, Willem-Pier Vellinga, Holger Klinck, Tom Denton, Juan Sebastián Cañas, Giulio Martellucci, Fabrice Vinatier, Pierre Bonnet, Alexis Joly
Temporal shift in Eurasian Blackbird <i>Turdus merula</i> and European Robin <i>Erithacus rubecula</i> song under urban pressures
Bird Study· 2025DOI
Laurent Godet, Abel Prampart, Yasmine Benhamadi, Vincent Lostanlen, Pierre Aumond
Rebound Effects Make Digital Audio Unsustainable
2024 IEEE 5th International Symposium on the Internet of Sounds (IS2)· 2024DOI
Vincent Lostanlen, Lucie Bouchet
Sound evidence for biodiversity monitoring
Science· 2024DOI
Jeppe H. Rasmussen, Dan Stowell, Elodie F. Briefer
Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors
Emerging Bioacousticians Days (JJBA)
Vincent Kather, Burooj Ghani, Dan Stowell
Other Results (1)
Periodic Reporting for period 1 - BioacAI (Bioacoustic AI for wildlife protection)
Deliverables (1)
Websites, patent fillings, videos etc.