CorpusVis : Visual Analysis of Digital Sheet Music Collections

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Computer Graphics Forum. The Eurographics Association. 2022, 41(3), pp. 283-294. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/cgf.14540
Zusammenfassung

Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.

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ISO 690MILLER, Matthias, Julius RAUSCHER, Daniel A. KEIM, Mennatallah EL-ASSADY, 2022. CorpusVis : Visual Analysis of Digital Sheet Music Collections. In: Computer Graphics Forum. The Eurographics Association. 2022, 41(3), pp. 283-294. ISSN 0167-7055. eISSN 1467-8659. Available under: doi: 10.1111/cgf.14540
BibTex
@article{Miller2022-03-23T18:41:36ZCorpu-57067,
  year={2022},
  doi={10.1111/cgf.14540},
  title={CorpusVis : Visual Analysis of Digital Sheet Music Collections},
  number={3},
  volume={41},
  issn={0167-7055},
  journal={Computer Graphics Forum},
  pages={283--294},
  author={Miller, Matthias and Rauscher, Julius and Keim, Daniel A. and El-Assady, Mennatallah},
  note={German Research Foundation (DFG) within the project Knowledge Generation in Visual Analytics (Project-ID 350399414)}
}
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German Research Foundation (DFG) within the project Knowledge Generation in Visual Analytics (Project-ID 350399414)
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