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.

翻译：在这次研究中,我们提出了一个新型的半监督培训方法,使用与目标数据或没有目标标签的数据完全不同的分类分布的非标签数据。为此,我们引入了对比性正规化,旨在针对任务,同时培训。此外,我们提出了基于音频混合的简单增强功能战略,在批量样本中实施。实验结果证实,拟议方法成功地促进了性能的改善,特别是表明它具有稳定培训和概括化的优势。