Hannes Kath

Machine Learning for Passive Acoustic Wildlife Monitoring: Methods for Semi-Automated Population and Species Assessment

Passive acoustic monitoring (PAM) has become a powerful tool for studying wildlife by continuously recording environmental soundscapes. However, analysing large acoustic datasets remains highly time-consuming, as recordings are often annotated manually by domain experts. In this work, we investigate how machine learning can support scalable biodiversity monitoring by enabling efficient Read more

By Franziska Scheurer, ago

Xprize Rainforest Finalists

Researchers from IML Oldenburg, together with doctoral students from the Applied Artificial Intelligence (AAI) department at the University of Oldenburg, have reached the finals of XPRIZE Rainforest, a global competition that promotes innovative technologies for researching, assessing and conserving biodiversity in tropical rainforests. As part of the Brazilian Team, they Read more

By Anika Heinen-Hilgemeyer, ago

Interactive annotation of passive acoustic monitoring datasets

Passive acoustic monitoring (PAM), the recording of sounds using microphones (e.g. in biosphere reserves), is an increasingly popular method for continuous, reproducible, scalable and cost-effective monitoring of wildlife [Sugai et al., 2018]. It is widely employed in various fields, including ecology, marine biology, and conservation, to study animal behavior, biodiversity, Read more

By Franziska Scheurer, ago