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rct: random consistency training for semi-supervised sound event detection

2021-10-21 13:50:35
Nian Shao, Erfan Loweimi, Xiaofei Li
arXiv_SD
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.11144

PDF

https://arxiv.org/pdf/2110.11144.pdf

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