Chord Recognition with Deep Learning

TL;DR

This paper investigates the challenges in automatic chord recognition using deep learning, highlighting issues with rare chords, the benefits of pitch augmentation, and potential of synthetic data, while improving interpretability and achieving top results.

Contribution

It provides a comprehensive analysis of current methods, tests hypotheses with recent generative models, and introduces interpretability enhancements in chord recognition.

Findings

Chord classifiers struggle with rare chords

Pitch augmentation improves accuracy

Synthetic data is promising for future research

Abstract

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.