
The author proposes ASiT, a novel self-supervised learning framework that combines local and global contextual information through group masked model learning and self-distillation to enhance audio representation and achieve state-of-the-art performance in various audio classification tasks.


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asit-local-global-audio-spectrogram-vision-transformer-for-event-classification


ASiT: Local-Global Audio Spectrogram Vision Transformer for Event Classification


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Transformers are adapted for audio tasks through self-supervised pretraining, enhancing performance in various classification tasks.
