3 papers
eess.AS2026
Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning
Shiqi Zhang, Marius Faiß, Ariana Strandburg-Peshkin +1
Bioacoustic call-type classification relies on costly expert annotation. Active learning can reduce this burden by selecting a small batch of segments for expert annotation and usi…
cs.SD2025
InsectSet459: an open dataset of insect sounds for bioacoustic machine learning
Marius Faiß, Burooj Ghani, Dan Stowell
Automatic recognition of insect sound could help us understand changing biodiversity trends around the world -- but insect sounds are challenging to recognize even for deep learnin…
cs.SD2024
animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics
Julian C. Schäfer-Zimmermann, Vlad Demartsev, Baptiste Averly +9
Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare.…