4 citations · 6 across the 3 of their papers we have counts for
5 papers
Uncertainty Calibration of Multi-Label Bird Sound Classifiers
Raphael Schwinger, Ben McEwen, Vincent S. Kather +3
Passive acoustic monitoring enables large-scale biodiversity assessment, but reliable classification of bioacoustic sounds requires not only high accuracy but also well-calibrated…
Foundation Models for Bioacoustics -- a Comparative Review
Raphael Schwinger, Paria Vali Zadeh, Lukas Rauch +4
Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This…
Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification
René Heinrich, Lukas Rauch, Raphael Schwinger +4
Adversarial training is a promising strategy for enhancing robustness against adversarial attacks, but its impact on generalization under substantial distribution shifts in audio c…
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Lukas Rauch, Raphael Schwinger, Moritz Wirth +8
Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domai…
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers
Lukas Rauch, Raphael Schwinger, Moritz Wirth +3
We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to…