Chord Recognition with Deep Learning
This work addresses the problem of automatic chord recognition for music analysis, but it is incremental as it builds on existing methods and focuses on specific improvements.
The paper tackles the slow progress in automatic chord recognition by analyzing existing deep learning methods and testing hypotheses with generative models, finding that chord classifiers struggle with rare chords and pitch augmentation improves accuracy, while also improving interpretability and reporting some of the best results in the field.
Progress in automatic chord recognition has been slow since the advent of deep learning in the field. To understand why, I conduct experiments on existing methods and test hypotheses enabled by recent developments in generative models. Findings show that chord classifiers perform poorly on rare chords and that pitch augmentation boosts accuracy. Features extracted from generative models do not help and synthetic data presents an exciting avenue for future work. I conclude by improving the interpretability of model outputs with beat detection, reporting some of the best results in the field and providing qualitative analysis. Much work remains to solve automatic chord recognition, but I hope this thesis will chart a path for others to try.