Papers › EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
Wenxi Chen, Yuzhe Liang, Ziyang Ma, Zhisheng Zheng, Xie Chen
Audio self-supervised learning (SSL) pre-training, which aims to learn good representations from unlabeled audio, has made remarkable progress. However, the extensive computational demands during pre-training pose a significant barrier to the potential application and optimization of audio SSL models. In this paper, inspired by the success of data2vec 2.0 in image modality and Audio-MAE in audio modality, we introduce Efficient Audio Transformer (EAT) to further improve the effectiveness and efficiency in audio SSL. The proposed EAT adopts the bootstrap self-supervised training paradigm to the audio domain. A novel Utterance-Frame Objective (UFO) is designed to enhance the modeling capability of acoustic events. Furthermore, we reveal that the masking strategy is critical in audio SSL pre-training, and superior audio representations can be obtained with large inverse block masks. Experiment results demonstrate that EAT achieves state-of-the-art (SOTA) performance on a range of audio-related tasks, including AudioSet (AS-2M, AS-20K), ESC-50, and SPC-2, along with a significant pre-training speedup up to ~15x compared to existing audio SSL models.
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Code
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Code Syntology ran Syntology
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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 | AudioSet | EAT | Test mAP | 0.486 | #18 of 51 | Archive leaderboard | report |
| Audio Classification | Balanced Audio Set | EAT | Mean AP | 40.3 | #3 of 8 | Archive leaderboard | report |
| Audio Classification | ESC-50 | EAT | Accuracy (5-fold) | 96.0 | #15 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | EAT | PRE-TRAINING DATASET | AudioSet | #15 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | EAT | Top-1 Accuracy | 96.0 | #15 of 29 | Archive leaderboard | report |
| Audio Classification | Speech Commands | EAT | Accuracy | 98.3±0.04 | #1 of 7 | 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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