# From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation

arXiv: https://arxiv.org/abs/2509.01588 · HTML: https://mlsanity.com/paper/2509.01588/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Self-supervised Learning with Random-projection Quantizer for Speech Recognition](https://mlsanity.com/paper/2202.01855/) (2022)
- [A Foundation Model for Music Informatics](https://mlsanity.com/paper/2311.03318/) (2023)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [A Fully Convolutional Deep Auditory Model for Musical Chord Recognition](https://mlsanity.com/paper/1612.05082/) (2016)
- [Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations](https://mlsanity.com/paper/1706.09552/) (2017)
- [Class-Similarity Based Label Smoothing for Confidence Calibration](https://mlsanity.com/paper/2006.14028/) (2020)
- [Using musical relationships between chord labels in automatic chord extraction tasks](https://mlsanity.com/paper/1911.04973/) (2019)
- [Exploiting Time-Frequency Conformers for Music Audio Enhancement](https://mlsanity.com/paper/2308.12599/) (2023)
- [ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio](https://mlsanity.com/paper/2408.00674/) (2024)
- [ChordFormer: A Conformer-Based Architecture for Large-Vocabulary Audio Chord Recognition](https://mlsanity.com/paper/2502.11840/) (2025)

## Built on by (most cited first, 3 of 3)

- [What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models](https://mlsanity.com/paper/2608.14819/) (2026)
- [Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding](https://mlsanity.com/paper/2608.25621/) (2026)
- [JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings](https://mlsanity.com/paper/2610.15356/) (2026)

## Abstract (arXiv)

Audio Chord Estimation (ACE) holds a pivotal role in music information research, having garnered attention for over two decades due to its relevance for music transcription and analysis. Despite notable advancements, challenges persist in the task, particularly concerning unique characteristics of harmonic content, which have resulted in existing systems' performances reaching a glass ceiling. These challenges include annotator subjectivity, where varying interpretations among annotators lead to inconsistencies, and class imbalance within chord datasets, where certain chord classes are over-represented compared to others, posing difficulties in model training and evaluation. As a first contribution, this paper presents an evaluation of inter-annotator agreement in chord annotations, using metrics that extend beyond traditional binary measures. In addition, we propose a consonance-informed distance metric that reflects the perceptual similarity between harmonic annotations. Our analysis suggests that consonance-based distance metrics more effectively capture musically meaningful agreement between annotations. Expanding on these findings, we introduce a novel ACE conformer-based model that integrates consonance concepts into the model through consonance-based label smoothing. The proposed model also addresses class imbalance by separately estimating root, bass, and all note activations, enabling the reconstruction of chord labels from decomposed outputs.