Active Learning for Sound Event Detection

@article{Shuyang2020ActiveLF,
  title={Active Learning for Sound Event Detection},
  author={Zhao Shuyang and Toni Heittola and Tuomas Virtanen},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2020},
  volume={28},
  pages={2895-2905}
}
This article proposes an active learning system for sound event detection (SED). It aims at maximizing the accuracy of a learned SED model with limited annotation effort. The proposed system analyzes an initially unlabeled audio dataset, from which it selects sound segments for manual annotation. The candidate segments are generated based on a proposed change point detection approach, and the selection is based on the principle of mismatch-first farthest-traversal. During the training of SED… Expand
1 Citations
FSD50K: an Open Dataset of Human-Labeled Sound Events
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