Bridging the information gap between structural and note-level musical datasets

Hu, Yuerong; Weigl, David M.; Page, Kevin R.; Dubnicek, Ryan; Downie, J. Stephen

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https://hdl.handle.net/2142/103320 Copy

Description

Title

Bridging the information gap between structural and note-level musical datasets

Author(s)

• Hu, Yuerong
• Weigl, David M.
• Page, Kevin R.
• Dubnicek, Ryan
• Downie, J. Stephen

Issue Date

2019-03-15

Keyword(s)

• Linked data
• Music information
• Digital musicology
• McGill Billboard Project
• MIDI linked data cloud

Abstract

While there are an increasing number of datasets containing various features of musical information, the lack of connections between them remains a barrier to their use in research. For example, one dataset might encode the identification of structural segments by musicologists in audio recordings, while another dataset could contain a symbolic encoding of the music notation being played in that audio recording. Without explicit connections, there is a significant extra work in realizing their potential for musicological study. In this paper we investigate how Linked Data can be used to implement such connections, specifically between the McGill Billboard corpus of structural annotations and the MIDI Linked Data Cloud (MIDI-LD). Firstly, we republish structural information from Billboard as RDF. We then align this structural data with matching symbolic encodings in MIDI-LD; before finally linking individual structural annotations from Billboard to note-level sections in the MIDI-LD. Our alignments enable cross-referencing and combined queries for musicological analysis across the enriched union dataset, and serve as a model for the creation of information resources comprising musical structures at varying granularity.

Publisher

iSchools

Series/Report Name or Number

iConference 2019 Proceedings

Type of Resource

text

Language

eng

Permalink

http://hdl.handle.net/2142/103320

DOI

https://doi.org/10.21900/iconf.2019.103320

Copyright and License Information

Copyright 2019 Yuerong Hu, David M. Weigl, Kevin R. Page, Ryan Dubnicek, and Stephen J. Downie

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