Alle

Our Paper Can Masked Autoencoders Also Listen to Birds? by Lukas Rauch, Ilyass Moummad, René Heinrich, Alexis Joly, Bernhard Sick and Christoph Scholz has been accepted at Transactions on Machine Learning Research. Abstract: Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task. However, general-purpose models fail to generalize well […]

Our Paper Adversarial Training Improves Generalization Under Distribution Shifts in Bioacoustics by René Heinrich, Lukas Rauch, Bernhard Sick and Christoph Scholz is online on ArXiv. Abstract: Adversarial training is a promising strategy for enhancing model robustness against adversarial attacks. However, its impact on generalization under substantial data distribution shifts in audio classification remains largely unexplored.

Our Paper Foundation Models for Bioacoustics — a Comparative Review by Raphael Schwinger, Paria Vali Zadeh, Lukas Rauch, Mats Kurz, Tom Hauschild, Sam Lapp and Sven Tomforde has been submitted to Ecological Informatics. Abstract: Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks.

The Paper Continental-scale behavioral response of birds to a total solar eclipse by David Mann, Austin Anderson, Amy Donner, Michael Hall, Stefan Kahl and Holger Klinck will appear in volume 15 of Scientific Reports. Abstract: Based on anecdotal evidence and research involving human observation and community science, radar analyses, and acoustical studies, birds are thought

Our publication Assessing spatial variability and efficacy of surrogate species at an ecosystem scale by Kristin Brunk, H. Kramer, M. Peery, Stefan Kahl and Connor Wood will appear in the August Volume of Conservation Biology. Abstract: Preserving biodiversity is a central goal of conservation, but, in practice, monitoring biodi-versity often involves assessing population trends for one or

Continuous Real-Time Acoustic Monitoring of endangered bird species in Hawai‘i will appear in volume 87 of Ecological Informatics. Abstract: The decline of endemic bird species in Hawai‘i requires innovative conservation measures enabled by passive acoustic monitoring (PAM). This paper describes a novel real-time PAM system used in the Pōhakuloa Training Area (PTA) to reduce wildlife collisions and minimize disruptions

Our paper BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics by Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich, Denis Huseljic, Marek Herde, Jonas Lange, Stefan Kahl, Bernhard Sick, Sven Tomforde and Christoph Scholz has been accepted at International Conference on Learning Representations (ICLR) 2025. Abstract: Deep learning (DL) has greatly advanced audio classification, yet […]

Our paper AudioProtoPNet: An interpretable deep learning model for bird sound classification by René Heinrich, Lukas Rauch, Bernhard Sick and Christoph Scholz is published in the 87th volume of Ecological Informatics. Abstract: Deep learning models have significantly advanced acoustic bird monitoring by recognizing numerous bird species based on their vocalizations. However, traditional deep learning models are