Deep Learning for Chord Recognition: Challenges and Insights

This paper investigates the limitations of deep learning in automatic chord recognition, a field that has seen slow progress. It explores the performance of existing methods, the impact of data augmentation, and the potential of generative models. The study highlights the poor performance on rare chords and the benefits of pitch augmentation. It also suggests that synthetic data could be a promising direction for future research. The paper aims to improve the interpretability of model outputs and provides state-of-the-art results.