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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2110.11144 (eess)
[Submitted on 21 Oct 2021 (v1), last revised 27 Mar 2022 (this version, v3)]

Title:RCT: Random Consistency Training for Semi-supervised Sound Event Detection

Authors:Nian Shao, Erfan Loweimi, Xiaofei Li
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Abstract:Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency. The integration of semi-supervised learning (SSL) largely mitigates such problem while bringing no extra annotation budget. This paper researches on several core modules of SSL, and introduces a random consistency training (RCT) strategy. First, a self-consistency loss is proposed to fuse with the teacher-student model to stabilize the training. Second, a hard mixup data augmentation is proposed to account for the additive property of sounds. Third, a random augmentation scheme is applied to flexibly combine different types of data augmentations. Experiments show that the proposed strategy outperform other widely-used strategies.
Comments: Preprint for interspeech 2022
Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2110.11144 [eess.AS]
  (or arXiv:2110.11144v3 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2110.11144

Submission history

From: Nian Shao [view email]
[v1] Thu, 21 Oct 2021 13:50:35 UTC (492 KB)
[v2] Thu, 4 Nov 2021 05:27:26 UTC (115 KB)
[v3] Sun, 27 Mar 2022 08:34:09 UTC (610 KB)
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