A collection of my research projects in AI, bioacoustics, and biodiversity conservation. All the code I produce is hoted on our institutional GitHub NINAnor
I'm always open to new collaborations and research opportunities. If you're interested in working together on AI for conservation or bioacoustic analysis, please don't hesitate to reach out!
Get in TouchSustainable Forest Practices and Nature Restoration on Peat Soils, using bioacoustics as a way of monitoring recovery
Using turbulent eddies to create paths for safe downstream migration for salmonids and eels past hydropower intakes
EU Horizon project extending biodiversity data collection in all modalities (eDNA, acoustic, camera traps)
Few-shot bioacoustic event detection system for rare species monitoring using advanced deep learning techniques.
A deep learning-powered voice activity detection algorithm specifically designed for ecological soundscape analysis.
A model to detect whether snowmobiles are present in near-real time
A high level API for training BEATs, a powerful deep-learning acoustic classifier
An R Shiny application designed to calculate and visualize carbon storage in peatland areas.
Standardisation of passive acoustic monitoring methods, including detection space modelling around acoustic recorders and development of metadata standards for large-scale PAM networks.
Pretrained Audio Embedding Models for Sound Event Detection of Arctic Fox Vocalisations — Master's thesis in Electronics Systems Design and Innovation. Supervisor: Guillaume Dutilleux; Co-supervisor: Benjamin Cretois.
Passive acoustic monitoring as a complementary approach to assess phenology in bird migration — AgroBioTech.