Benjamin Cretois: Leveraging AI for Large-Scale Acoustic Biodiversity Monitoring

Advancing biodiversity monitoring is crucial for meeting the EU Biodiversity Strategy targets and addressing gaps in current ecological assessments. However, collecting data to monitor the state of biodiversity is time and resource consuming. Passive Acoustic Monitoring (PAM), in combination with AI tools offers an efficient alternative to conventional data collection practices. PAM is a non-invasive method that uses sound recorders to capture wildlife vocalizations and environmental sounds over time. It is particularly valuable for monitoring elusive or nocturnal species, such as birds, amphibians, and marine mammals, that are challenging to detect visually.

The "Towards a Transnational Acoustic Biodiversity Monitoring Network" (TABMON) project is an initiative to establish a transnational passive acoustic monitoring monitoring network using autonomous acoustic sensors across four different European countries: Norway, Netherlands, France and Spain. TABMON’s objective is to demonstrate how acoustic sensing, coupled with cutting-edge AI, can complement traditional monitoring methods and support the development of methods to better monitor biodiversity.

In this talk, we will also share our experiences with the deployment of acoustic recorders, data management strategies, and annotation protocols. These include managing large-scale, networked deployments across diverse landscapes, designing an efficient annotation workflow, and leveraging AI tools to process and analyze massive datasets.

Benjamin Cretois is a researcher at the Norwegian Institute for Nature Research (NINA), working at the interface of artificial intelligence, statistics, and biodiversity conservation. His work primarily focuses on developing deep learning tools to better analyze bioacoustic data.