# CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions

arXiv: https://arxiv.org/abs/2410.22046 · HTML: https://mlsanity.com/paper/2410.22046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions](https://mlsanity.com/paper/2205.14700/) (2022)
- [Perceptual Musical Features for Interpretable Audio Tagging](https://mlsanity.com/paper/2312.11234/) (2023)
- [Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction](https://mlsanity.com/paper/2102.02917/) (2021)
- [Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education](https://mlsanity.com/paper/2306.07310/) (2023)
- [A Large-Scale Study of Language Models for Chord Prediction](https://mlsanity.com/paper/1804.01849/) (2018)

## Abstract (arXiv)

Chord progressions encapsulate important information about music, pertaining to its structure and conveyed emotions. They serve as the backbone of musical composition, and in many cases, they are the sole information required for a musician to play along and follow the music. Despite their importance, chord progressions as a data domain remain underexplored; existing datasets lack the scale, structural annotation, and metadata diversity required for rigorous evaluation of music understanding models. In this work, we present Chordonomicon, the largest dataset of its kind, containing over 666,000 song-level symbolic chord progressions, annotated with structural parts (verse, chorus, bridge, etc.), genre, and release date, created by scraping various sources of user-generated progressions and associated metadata, showing strong similarity to well-established prior datasets. Beyond the dataset itself, we propose a reproducible benchmark suite for next chord prediction, evaluating three sequence modeling architectures (RNN, GRU, LSTM) across multiple context window sizes and data scales under strict exact-match evaluation. Our experiments reveal that structural part annotations consistently improve prediction performance. Chordonomicon is released as an open benchmark, providing split methodology, baselines, and evaluation protocols to enable fair and reproducible comparison for future work on chord prediction, classification, generation, and beyond.