Leonie Baier

I'm a neuroethologist and bioacoustician fascinated by how evolutionary pressures have shaped the sensory worlds and behaviours of bats. With a background in auditory virtual reality, signal processing, and sensory ecology, I now focus on using sound-based machine learning to support wildlife monitoring and biodiversity conservation to create real-world impact.

Keywords

Bats, biosonar, bioacoustics, machine learning, deep learning, AI, audio, neuroethology

Research
interest

I'm part of the EU-funded MAMBO project, developing machine learning tools to detect and classify bat vocalisations: advancing bioacoustic monitoring to support more effective, data-driven biodiversity conservation efforts.

Bats are key players in ecosystem health, yet their nocturnal, aerial, and often cryptic behaviour makes them particularly difficult to monitor. This monitoring gap limits our ability to detect population changes, including responses to threats such as habitat loss and insect decline. Improving how we track bat populations is therefore essential for effective conservation planning. Advances in technology are revolutionizing how we monitor biodiversity, making it easier and more cost-effective to track species remotely. As part of the MAMBO project, we are developing AI tools that use sound for the remote monitoring of bats. By harnessing sound recognition and deep learning, we aim to enable faster and more accurate species detection. Our goal is to expand biodiversity monitoring efforts, supporting the EU Biodiversity Strategy for 2030.