title: PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music
    Processing
summary: 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_authors._id: 66ea3ac15b8862e3ce7f6042
paper_authors.name: Phillip Long
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:07:28.010Z
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paper_authors.fullname: Phillip Long
paper_authors.type: user
paper_authors.user: p1long
paper_authors._id: 66ea3ac15b8862e3ce7f6043
paper_authors.name: Zachary Novack
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Zachary Novack
paper_authors.type: user
paper_authors.user: ZacharyNovack
paper_authors._id: 66ea3ac15b8862e3ce7f6044
paper_authors.name: Taylor Berg-Kirkpatrick
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-09-18T02:28:18.364Z
paper_authors._id: 66845caa4b898a41b7af775c
paper_authors.avatarUrl: /avatars/e205290cd372b6b464509de279f6642d.svg
paper_authors.fullname: Taylor Berg-Kirkpatrick
paper_authors.type: user
paper_authors.user: tberg12
paper_authors._id: 66ea3ac15b8862e3ce7f6045
paper_authors.name: Julian McAuley
paper_title: PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music
    Processing
paper_summary: 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_ai_summary: PDMX, a large-scale open-source dataset of public domain MusicXML scores, facilitates multitrack music generation research by providing extensive metadata and user ratings as measures of data quality.
paper_ai_keywords: MusicXML multitrack music generation