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From the 2 of 6 linked papers with an AI index.

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20242026
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eess.AS2026

Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification

Shiqi Zhang, Tuomas Virtanen

The paper studies active learning for frame‑level sound event detection and shows that the common mismatch‑first farthest‑traversal strategy performs poorly under limited labeling…

eess.AS2026

Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning

Shiqi Zhang, Marius Faiß, Ariana Strandburg-Peshkin +1

The paper introduces BADGE‑Greedy‑DPP, a deterministic batch selection method that greedily maximizes the volume of gradient embeddings to improve active learning for sparse, long‑…

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…

eess.AS2026

Learning Input-Channel Permutation Equivariance for Multi-Channel Source Separation: Reducing Bleeding in Small Music Ensembles

Ruchi Pandey, Jaime Garcia-Martinez, Pablo Cabanas-Molero +5

Microphone bleed is a persistent challenge in small ensembles and orchestral recordings, where close microphones intended for individual instruments also capture leakage from nearb…