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

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/.