Papers › Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
Florian Schmid, Khaled Koutini, Gerhard Widmer
Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We propose a training procedure for efficient CNNs based on offline Knowledge Distillation (KD) from high-performing yet complex transformers. The proposed training schema and the efficient CNN design based on MobileNetV3 results in models outperforming previous solutions in terms of parameter and computational efficiency and prediction performance. We provide models of different complexity levels, scaling from low-complexity models up to a new state-of-the-art performance of .483 mAP on AudioSet. Source Code available at: https://github.com/fschmid56/EfficientAT
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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 | mn40_as (Ensemble) | Test mAP | 0.498 | #10 of 51 | Archive leaderboard | report |
| Audio Classification | AudioSet | mn40_as (Single) | Test mAP | 0.483 | #24 of 51 | Archive leaderboard | report |
| Audio Classification | ESC-50 | mn40_as | Accuracy (5-fold) | 97.45 | #6 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | mn40_as | PRE-TRAINING DATASET | AudioSet | #6 of 29 | Archive leaderboard | report |
| Audio Classification | ESC-50 | mn40_as | Top-1 Accuracy | 97.45 | #6 of 29 | Archive leaderboard | report |
| Audio Tagging | AudioSet | mn40_as (Ensemble) | mean average precision | 0.498 | #2 of 11 | Archive leaderboard | report |
| Audio Tagging | AudioSet | mn40_as (Single) | mean average precision | 0.483 | #6 of 11 | 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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