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title subtitle author header-includes output
RFC on Music Score Pipeline
Time Machine RFC-0036
Juha Henriksson
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Motivation

This RFC defines the general architecture for music scores. The document deals with how to store music notation that is in digital format. The recommended file formats not only promote long-term preservation and access of digital notated music, but also ensure compatibility between different software as well as offer opportunities for versatile use, for example for research purposes.

Digital music scores

Sheet music in digital form can be created with scorewriting programs, optical music recognition (OMR) software 1, or music sequencer software. Many of these softwares have their own storage format, while others use more commonly used storage formats.

Digital notated music is recommended to be stored in an XML-based markup music notational format. Library of Congress recommends 23 MusicXML 4 and the Music Encoding Initiative (MEI) 5, in that order. Both these formats make it possible to preserve and access digital notation also in the coming years.

For preservation purposes, it might be reasonable to save the notated music as a PDF/A file as well 6. In addition, if the original sheet music was written with a notation software such as Sibelius, Finale, Dorico, or MuseScore, it is recommended to preserve the original files of that program as well, just to be safe.

Files must not contain any digital rights management technologies, encryption, or other measures that control access to or prevent use of the digital score.

MusicXML

MusicXML is an XML-based file format for representing Western musical notation. MusicXML was designed from the ground up for sharing sheet music files between applications, and for archiving sheet music files for use in the future.

MusicXML is very widely used, which guarantees compatibility between many different software. Over 260 different software features at least some MusicXML interchange capability 7. These include most scorewriting programs such as Sibelius, Finale, and MuseScore, most optical music recognition programs, and most music sequencer programs.

Many of the above-mentioned programs can both read and write MusicXML format. There are also plugins for Sibelius and Finale available [mxml1]. The MusicXML document type definitions (DTD) and XML Schema Definitions (XSD) are each freely redistributable, and the files are available from the W3C MusicXML GitHub repository 8. W3C Comminity Group has published a tutorial 9 to software developers who are interesting in reading or writing MusicXML files. However, the advantage of MusicXML is that users need no knowledge of coding or XML to save or open MusicXML files. If the software has MusicXML read/write capabilities, it can open and/or create files in this format 10.

Music Encoding Iniative (MEI)

The Music Encoding Initiative (MEI) 11 is a public standard controlled by the scholarly community. Even though MusicXML may be considered the preferred form of permanent preservation of digital sheet music over MEI, it may be advisable to convert the music also to the MEI schema, because it opens music to new forms of study gaining prevalence in the digital humanities, in which researchers use automation to explore and interact with collection materials and descriptions as data 12.

MEI defines a system for encoding musical documents in a machine-readable structure. It is based on open standards and is platform-independent. MEI is not limited to modern standard notation. Neumes, tablature, and mensural notation can be encoded as well 12.

MEI contains the same functionality as MusicXML in terms of notation and page layout, but it also can encode information about the notation and its intellectual content in a structured and systematic way. The MEI schema 13 is a core set of rules for recording physical and intellectual characteristics of music notation documents expressed as an XML schema.

The MEI schema is complemented by the MEI Guidelines 14, which provide detailed explanations of the components of the MEI model and best practices suggestions. The MEI community also provides a wide range of tools 15 for working with MEI data. They can serve a whole range of purposes, from data creation or conversion to rendering or analysis.

The Music Encoding Initiative's online tool Verovio 16 provides instant rendering from MEI to notated music and conversion from MusicXML to MEI. In Verovio, the MEI file can also be converted to a PDF. So far, only Sibelius notation software can directly export files in MEI format.

Metadata

The stored files should be equipped with a sufficient metadata 2, which supports their future use and research. The recommended metadata includes (if available): title, creator, creation date or start date/end date, place of publication, publisher/producer/distributor, ISMN and other relevant identifiers, instrumentation, language of work, edition, and subject descriptors. Furthermore, also events and abstracts are recommended to be included, if available.

Optical Music Recognition

Optical Music Recognition (OMR) is a field of research that investigates how to computationally read music notation in documents 17. OMR is a challenging process that differs in difficulty from OCR and HTR because of the properties of music notation as a contextual writing system. First, the visual expression of music is very diverse. For instance, the Standard Music Font Layout 18 currently lists over 2440 recommended characters, plus several hundred optional glyphs. Second, it is only in their configuration – how they are placed and arranged on the staves, and with respect to each other – that specifies what notes should be played.

The two main goals of OMR are:

  • Recovering music notation and information from the engraving process, i.e., what elements were selected to express the given piece of music and how they were laid out. The output format must be capable of storing music notation, e.g., MusicXML or MEI.
  • Recovering musical semantics (i.e., the notes, represented by their pitches, velocities, onsets, and durations). MIDI would be an appropriate output representation for this goal.

Automatic Music Trascription

Automatic Music Transcription (AMT) is the process of automatically converting audio recordings of music into symbolic representations, such as sheet music (e.g., MusicXML or MEI) or MIDI files. AMT is a very useful tool for music analysis. AMT comprises several subtasks, including (multi-)pitch estimation, onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting. Due to the very nature of music signals, which often contain several sound sources that produce one or more concurrent sound events that are meant to be highly correlated over both time and frequency, AMT is still considered a challenging and open problem. 19

Footnotes

  1. https://en.wikipedia.org/wiki/Optical_music_recognition ↩

  2. https://www.loc.gov/preservation/resources/rfs/musical-scores.html ↩ ↩2

  3. https://guides.loc.gov/music-notation-preferred-preservation-formats-for-digital-scores/introduction ↩

  4. https://www.musicxml.com ↩

  5. https://music-encoding.org ↩

  6. https://guides.loc.gov/music-notation-preferred-preservation-formats-for-digital-scores/pdf-a ↩

  7. https://www.musicxml.com/software/ ↩

  8. https://www.musicxml.com/for-developers/ ↩

  9. https://www.w3.org/2021/06/musicxml40/tutorial/introduction/ ↩

  10. https://guides.loc.gov/music-notation-preferred-preservation-formats-for-digital-scores/musicxml ↩

  11. https://music-encoding.org/about/ ↩

  12. https://guides.loc.gov/music-notation-preferred-preservation-formats-for-digital-scores/music-encoding-initiative ↩ ↩2

  13. https://music-encoding.org/resources/schemas.html ↩

  14. https://music-encoding.org/guidelines/v4/content/ ↩

  15. https://music-encoding.org/resources/tools.html ↩

  16. https://www.verovio.org/index.xhtml ↩

  17. https://dl.acm.org/doi/10.1145/3397499 ↩

  18. https://w3c.github.io/smufl/latest/index.html ↩

  19. https://ieeexplore.ieee.org/document/8588423 ↩