  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
