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Cross-domain Semi-Supervised Audio Event Classification Using Contrastive Regularization

2021-09-29 15:43:12
Donmoon Lee, Kyogu Lee
arXiv_SD
       
Abstract

In this study, we proposed a novel semi-supervised training method that uses unlabeled data with a class distribution that is completely different from the target data or data without a target label. To this end, we introduce a contrastive regularization that is designed to be target task-oriented and trained simultaneously. In addition, we propose an audio mixing based simple augmentation strategy that performed in batch samples. Experimental results validate that the proposed method successfully contributed to the performance improvement, and particularly showed that it has advantages in stable training and generalization.

Abstract (translated)

URL

https://arxiv.org/abs/2109.14508

PDF

https://arxiv.org/pdf/2109.14508.pdf

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