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In this study, we proposed a novel semi-supervised training method that usesunlabeled data with a class distribution that is completely different from thetarget data or data without a target label. To this end, we introduce acontrastive regularization that is designed to be target task-oriented andtrained simultaneously. In addition, we propose an audio mixing based simpleaugmentation strategy that performed in batch samples. Experimental resultsvalidate that the proposed method successfully contributed to the performanceimprovement, and particularly showed that it has advantages in stable trainingand generalization.

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