Spectral-domain augmentation for cover song identification

Abstract

In many applications, data imbalance and annotation challenges limit the size of training datasets, hindering the ability of deep neural networks to fully leverage their representational capacity. Data augmentation is a widely used countermeasure that generates additional training samples by manipulating existing data. This paper investigates spectral-domain data augmentation methods specifically for cover song identification task, enabling on-the-fly augmentation with minimal computational overhead. We explore various spectral modifications and mixing techniques, applying them directly in the frequency domain, and evaluate their effectiveness on two cover song identification datasets. Among the augmentation methods tested, a mixing approach involving cut-and-paste operations in the spectral domain achieved the highest accuracy, demonstrating the potential of spectral augmentations to enhance the performance of neural networks for cover song identification.