AudRandAug: Random Image Augmentations for Audio Classification
Teerath Kumar Muhammad Turab Alessandra Mileo Malika Bendechache Takfarinas Saber
Data augmentation has proven to be effective in training neural networks.
Recently, a method called RandAug was proposed, randomly selecting data
augmentation techniques from a predefined search space. RandAug has
demonstrated significant performance improvements for image-related tasks while
imposing minimal computational overhead. However, no prior research has
explored the application of RandAug specifically for audio data augmentation,
which converts audio into an image-like pattern. To address this gap, we
introduce AudRandAug, an adaptation of RandAug for audio data. AudRandAug
selects data augmentation policies from a dedicated audio search space. To
evaluate the effectiveness of AudRandAug, we conducted experiments using
various models and datasets. Our findings indicate that AudRandAug outperforms
other existing data augmentation methods regarding accuracy performance.