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While efficient architectures and a plethora of augmentations for end-to-endimage classification tasks have been suggested and heavily investigated,state-of-the-art techniques for audio classifications still rely on numerousrepresentations of the audio signal together with large architectures,fine-tuned from large datasets. By utilizing the inherited lightweight natureof audio and novel audio augmentations, we were able to present an efficientend-to-end network with strong generalization ability. Experiments on a varietyof sound classification sets demonstrate the effectiveness and robustness ofour approach, by achieving state-of-the-art results in various settings. Publiccode is available at:\href{https://github.com/Alibaba-MIIL/AudioClassfication}{this http url}

End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network