Automatic Audio Augmentation for Requests Sub-Challenge

This paper presents our solution for the Requests Sub-challenge of the ACM Multimedia 2023 Computational Paralinguistics Challenge. Drawing upon the framework of self-supervised learning, we put forth an automated data augmentation technique for audio classification, accompanied by a multi-channel fusion strategy aimed at enhancing overall performance. Specifically, to tackle the issue of imbalanced classes in complaint classification, we propose an audio data augmentation method that generates appropriate augmentation strategies for the challenge dataset. Furthermore, recognizing the distinctive characteristics of the dual-channel HC-C dataset, we individually evaluate the classification performance of the left channel, right channel, channel difference, and channel sum, subsequently selecting the optimal integration approach. Our approach yields a significant improvement in performance when compared to the competitive baselines, particularly in the context of the complaint task. Moreover, our method demonstrates noteworthy cross-task transferability.