6 papers
Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, René Heinrich, Houtan Ghaffari +4
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A ke…
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…
Can Masked Autoencoders Also Listen to Birds?
Lukas Rauch, René Heinrich, Ilyass Moummad +3
Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task. However, general-purpose models fai…
Adversarial Training Improves Generalization Under Distribution Shifts in Bioacoustics
René Heinrich, Lukas Rauch, Bernhard Sick +1
Adversarial training is a promising strategy for enhancing model robustness against adversarial attacks. However, its impact on generalization under substantial data distribution s…
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
Mohamed Hassouna, Clara Holzhüter, Malte Lehna +4
The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands ada…
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…