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<p>The present corpus is the outcome of a long-term collaborative effort to produce analytically annotated music scores suitable for the computer-assisted study of European compositions since 1600. With 1283 analytically annotated, symbolically encoded music scores by 36 composers, our corpus amounts to one of the largest published resources of its kind. At the same time, it provides a modular digital infrastructure for the accountable, collaborative curation of annotated scores (“sheet music”). All annotations were created and reviewed by a team of trained music theorists, who collaborated online using the <Emphasis FontCategory="NonProportional">git</Emphasis> version control software according to a formally codified workflow. To improve the consistency of analytical practices given the diversity of represented eras and genres, the corpus has been automatically parsed for notational well-formedness and cross-reviewed by annotators for adherence to our music-analytical guidelines. The computational infrastructure has been designed with “data persistence” and open access in mind.</p>

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A corpus and a modular infrastructure for the empirical study of (an)notated music

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月23日

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  • Johannes Hentschel,
  • Yannis Rammos,
  • Markus Neuwirth,
  • Martin Rohrmeier

摘要

The present corpus is the outcome of a long-term collaborative effort to produce analytically annotated music scores suitable for the computer-assisted study of European compositions since 1600. With 1283 analytically annotated, symbolically encoded music scores by 36 composers, our corpus amounts to one of the largest published resources of its kind. At the same time, it provides a modular digital infrastructure for the accountable, collaborative curation of annotated scores (“sheet music”). All annotations were created and reviewed by a team of trained music theorists, who collaborated online using the git version control software according to a formally codified workflow. To improve the consistency of analytical practices given the diversity of represented eras and genres, the corpus has been automatically parsed for notational well-formedness and cross-reviewed by annotators for adherence to our music-analytical guidelines. The computational infrastructure has been designed with “data persistence” and open access in mind.