Papers › ATST: Audio Representation Learning with Teacher-Student Transformer
ATST: Audio Representation Learning with Teacher-Student Transformer
Xian Li, Xiaofei Li
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data. SSL has achieved promising results in various domains. This work addresses the problem of segment-level general audio SSL, and proposes a new transformer-based teacher-student SSL model, named ATST. A transformer encoder is developed on a recently emerged teacher-student baseline scheme, which largely improves the modeling capability of pre-training. In addition, a new strategy for positive pair creation is designed to fully leverage the capability of transformer. Extensive experiments have been conducted, and the proposed model achieves the new state-of-the-art results on almost all of the downstream tasks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | Balanced Audio Set | Base (ours) | Mean AP | 37.4 | #5 of 8 | Archive leaderboard | report |
| Speaker Identification | VoxCeleb1 | ATST Base (ours) | Accuracy | 94.3 | #6 of 12 | Archive leaderboard | report |
| Speaker Identification | VoxCeleb1 | ATST Base (ours) | Top-1 (%) | 94.3 | #6 of 12 | Archive leaderboard | report |
| Spoken Command Recognition | Speech Command v2 | Base (ours) | Accuracy | 98.0 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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