Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SP-SEDT: Self-supervised Pre-training for Sound Event Detection
  Transformer   Recently, an event-based end-to-end model (SEDT) has been proposed for sound
event detection (SED) and achieves competitive performance. However, compared
with the frame-based model, it requires more training data with temporal
annotations to improve the localization ability. Synthetic data is an
alternative, but it suffers from a great domain gap with real recordings.
Inspired by the great success of UP-DETR in object detection, we propose to
self-supervisedly pre-train SEDT (SP-SEDT) by detecting random patches (only
cropped along the time axis). Experiments on the DCASE2019 task4 dataset show
the proposed SP-SEDT can outperform fine-tuned frame-based model. The ablation
study is also conducted to investigate the impact of different loss functions
and patch size.

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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph