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