Abstract

The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shortage, particularly for symbolic music data. To alleviate this issue, we present PDMX: a large-scale open-source dataset of over 250K public domain MusicXML scores collected from the score-sharing forum MuseScore, making it the largest available copyright-free symbolic music dataset to our knowledge. PDMX additionally includes a wealth of both tag and user interaction metadata, allowing us to efficiently analyze the dataset and filter for high quality user-generated scores. Given the additional metadata afforded by our data collection process, we conduct multitrack music generation experiments evaluating how different representative subsets of PDMX lead to different behaviors in downstream models, and how user-rating statistics can be used as an effective measure of data quality. Examples can be found at https://pnlong.github.io/PDMX.demo/.

Paper

PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing
Phillip Long, Zachary Novack, Taylor Berg-Kirkpatrick, Julian McAuley.
arXiv

Bibtex

Please cite both of the following papers if you use our dataset.

@inproceedings{long2024pdmx,
	author={Long, Phillip and Novack, Zachary and Berg-Kirkpatrick, Taylor and McAuley, Julian},
	booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
	title={{PDMX}: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing}, 
	year={2025},
	pages={1-5},
	keywords={Filtering;Data integrity;Companies;Signal processing;Filtering algorithms;Data collection;Explosions;Multiple signal classification;Speech processing;Capacity planning;symbolic music datasets;symbolic music generation;music copyright},
	doi={10.1109/ICASSP49660.2025.10890217}
	}
@article{xu2024generating,
    title={Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset},
    author={Xu, Weihan and McAuley, Julian and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo and Dong, Hao-Wen},
    journal={arXiv preprint arXiv:2410.02084},
    year={2024}
}
		

Code	Dataset