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

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arxiv 2204.11479 v5 pith:Q5LGLTII submitted 2022-04-25 cs.SD cs.CVeess.AS

Avi Gazneli , Gadi Zimerman , Tal Ridnik , Gilad Sharir , Asaf Noy This is my paper
classification cs.SDcs.CVeess.AS
keywords audioaugmentationsclassificationefficientend-to-endarchitectureslargenetwork
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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}

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Cited by 3 Pith papers

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