Generalized Specaugment via Multi-Rectangle Inverse Masking For Acoustic Scene Classification

Pil Moo Byun, Joon-Hyuk Chang

Abstract: In this paper, we present the multi-rectangle inverse masking (MRIM), an extension and generalization of the traditional SpecAugment technique, for acoustic scene classification. While SpecAugment, observed from its unmasked areas, primarily forms rectangles around the input corners, our novel strategy generates rectangles at random positions with varied sizes, enhancing the data augmentation capacity. Our evaluations, conducted on the DCASE 2019 and 2020 datasets using CNN architectures like ResNet50 and BC-Res2Net, highlighted notable performance enhancements. Importantly, our method demonstrated resilience even when post-processing masking is applied to unseen test data, emphasizing its robustness across diverse acoustic scenes. To gain a deeper understanding of our method’s impact, we utilize the grad-CAM++ technique, a tool from explainable AI, to explore how masking influences model activations.