Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification
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 the classifier most. One of the prevailing acquisition strategies for this task, mismatch-first farthest-traversal (MFFT), combines the disagreement between two classifiers and the diversity of the selected segments through hard sequential decisions. It selects whole groups of high-disagreement segments first and spreads only the remaining budget by farthest traversal. On two multi-label datasets we show that this design is blind to the similarity among the selected segments and fails under low budgets, with every...
| Authors | Shiqi Zhang, Tuomas Virtanen |
|---|---|
| Primary category | eess.AS |
| Published | 15 July 2026 |
| Last updated at source | 18 September 2026 |
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