Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guided learning for weakly-labeled semi-supervised sound event detection   We propose a simple but efficient method termed Guided Learning for
weakly-labeled semi-supervised sound event detection (SED). There are two
sub-targets implied in weakly-labeled SED: audio tagging and boundary
detection. Instead of designing a single model by considering a trade-off
between the two sub-targets, we design a teacher model aiming at audio tagging
to guide a student model aiming at boundary detection to learn using the
unlabeled data. The guidance is guaranteed by the audio tagging performance gap
of the two models. In the meantime, the student model liberated from the
trade-off is able to provide more excellent boundary detection results. We
propose a principle to design such two models based on the relation between the
temporal compression scale and the two sub-targets. We also propose an
end-to-end semi-supervised learning process for these two models to enable
their abilities to rise alternately. Experiments on the DCASE2018 Task4 dataset
show that our approach achieves competitive performance.

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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th