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Transformers have achieved promising results on a variety of tasks. However,the quadratic complexity in self-attention computation has limited theapplications, especially in low-resource settings and mobile or edge devices.Existing works have proposed to exploit hand-crafted attention patterns toreduce computation complexity. However, such hand-crafted patterns aredata-agnostic and may not be optimal. Hence, it is likely that relevant keys orvalues are being reduced, while less important ones are still preserved. Basedon this key insight, we propose a novel deformable audio Transformer for audiorecognition, named DATAR, where a deformable attention equipping with a pyramidtransformer backbone is constructed and learnable. Such an architecture hasbeen proven effective in prediction tasks,~\textit{e.g.}, event classification.Moreover, we identify that the deformable attention map computation mayover-simplify the input feature, which can be further enhanced. Hence, weintroduce a learnable input adaptor to alleviate this issue, and DATAR achievesstate-of-the-art performance.

Deformable Audio Transformer for Audio Event Detection