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 (e2e) 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:
https://github.com/Alibaba-MIIL/AudioClassification.
@article{Gazneli2022EAT,
title={End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network},
author={Avi Gazneli, Gadi Zimerman, Tal Ridnik, Gilad Sharir, Asaf Noy},
journal={arXiv preprint arXiv:2204.11479,},
year={2022},
}