# Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations

arXiv: https://arxiv.org/abs/1706.09552 · HTML: https://mlsanity.com/paper/1706.09552/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation](https://mlsanity.com/paper/2509.01588/) (2025)
- [ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio](https://mlsanity.com/paper/2408.00674/) (2024)
- [Learning Hierarchical Metrical Structure Beyond Measures](https://mlsanity.com/paper/2209.10259/) (2022)
- [Serenade: A Model for Human-in-the-loop Automatic Chord Estimation](https://mlsanity.com/paper/2310.11165/) (2023)
- [Training chord recognition models on artificially generated audio](https://mlsanity.com/paper/2508.05878/) (2025)
- [A Mamba-Based Model for Automatic Chord Recognition](https://mlsanity.com/paper/2601.02101/) (2026)

## Abstract (arXiv)

The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.