Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
One of the largest barriers to digital musicology is the time required to create an encoded music file. While tools exist to automate parts of the process, most of the symbolic content—pitches and rhythms—still needs to be entered manually, note by note. To facilitate the creation of corpora for dig...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
Text Encoding Initiative Consortium
2025-05-01
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| Series: | Journal of the Text Encoding Initiative |
| Subjects: | |
| Online Access: | https://journals.openedition.org/jtei/5832 |
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| Summary: | One of the largest barriers to digital musicology is the time required to create an encoded music file. While tools exist to automate parts of the process, most of the symbolic content—pitches and rhythms—still needs to be entered manually, note by note. To facilitate the creation of corpora for digital analysis, the authors have developed a procedure for encoding only the portions of a score relevant to a particular study. These encodings can then be extended at a later time, by any scholar who has access to them. Currently, there is no standard way to record metadata that detail which specific sections of a score have been encoded. This paper will introduce a pair of possible methods, constructed in the course of the authors’ research and tool development, to enhance the ability of MEI to accommodate these selective encodings. The first method takes advantage of MEI’s capacity to create customized schemas. The second, simpler method makes use of element entailments within current MEI structures and consists of additional documentation to clarify existing usage. The research project serves as a case study that illustrates the key assumptions underlying these two approaches, and how project-based considerations can lead to the adoption of one over the other. |
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| ISSN: | 2162-5603 |