Debiased Training For Semi-supervised Sound Event Detection

Abstract

Recently, semi-supervised sound event detection has attracted increasing research interest due to the scarcity of labeled data. However, traditional semi-supervised learning methods can lead to training instability and confirmation bias because of potentially incorrect pseudo labels. To address this issue, we propose the debiased training, a novel approach to reduce the inherent bias of pseudo labels. Debiased training can effectively decouple the generation and utilization of pseudo labels to mitigate the error accumulation and promote model’s robustness against biased pseudo labels. In addition, we introduce the channel restruction module (CRM) to decrease redundant computing and facilitate representation ability. Experimental results on DCASE 2023 task4 dataset show that the proposed methods significantly enhance the performance of semi-supervised methods while maintaining relatively low computational complexity.