3 papers
eess.AS2026
Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification
Shiqi Zhang, Tuomas Virtanen
Sound event detection relies on frame-level strong labels whose annotation is expensive. Active learning addresses this problem by selecting the audio segments whose labels help th…
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…
eess.AS2026
Mixture-Constrained Max Pooling Improves Separation-Based Bird Species Classification
Yuzhu Wang, Kalle Lahtinen, Patrik Lauha +4
Bird species classification from field recordings remains challenging due to overlapping vocalizations and incomplete species labels. We study source separation as a preprocessing…