Papers › AST: Audio Spectrogram Transformer
AST: Audio Spectrogram Transformer
Yuan Gong, Yu-An Chung, James Glass
In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.
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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 | AST (Ensemble) | Test mAP | 0.485 | #20 of 51 | Archive leaderboard | report |
| Audio Classification | AudioSet | AST (Single) | Test mAP | 0.459 | #37 of 51 | Archive leaderboard | report |
| Audio Classification | ESC-50 | Audio Spectrogram Transformer | Accuracy (5-fold) | 95.7 | #16 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | Audio Spectrogram Transformer | PRE-TRAINING DATASET | AudioSet, ImageNet | #16 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | Audio Spectrogram Transformer | Top-1 Accuracy | 95.7 | #16 of 29 | Archive leaderboard | report |
| Audio Classification | Speech Commands | AST-S | Accuracy | 98.11±0.05 | #2 of 7 | Archive leaderboard | report |
| Audio Tagging | AudioSet | Audio Spectrogram Transformer | mean average precision | 0.485 | #5 of 11 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Audio Spectrogram Transformer | Google Speech Commands V2 35 | 98.11 | #32 of 42 | Archive leaderboard | report |
| Speech Emotion Recognition | CREMA-D | ViT | Accuracy | 67.81 | #7 of 9 | Archive leaderboard | report |
| Time Series Analysis | Speech Commands | ViT | % Test Accuracy | 98.11 | #2 of 6 | 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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