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: Mark Saccomano, Lisa Rosendahl, David Lewis, Andrew Hankinson, Johannes Kepper, Kevin Page, Elisabete Shibata
Format: Article
Language:deu
Published: Text Encoding Initiative Consortium 2025-05-01
Series:Journal of the Text Encoding Initiative
Subjects:
Online Access:https://journals.openedition.org/jtei/5832
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author Mark Saccomano
Lisa Rosendahl
David Lewis
Andrew Hankinson
Johannes Kepper
Kevin Page
Elisabete Shibata
author_facet Mark Saccomano
Lisa Rosendahl
David Lewis
Andrew Hankinson
Johannes Kepper
Kevin Page
Elisabete Shibata
author_sort Mark Saccomano
collection DOAJ
description 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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series Journal of the Text Encoding Initiative
spelling doaj-art-e6b61191c7a742f98661d32577a2cb8a2025-08-20T02:33:05ZdeuText Encoding Initiative ConsortiumJournal of the Text Encoding Initiative2162-56032025-05-011810.4000/13x2dSelective Encoding: Reducing the Burden of Transcription for Digital MusicologistsMark SaccomanoLisa RosendahlDavid LewisAndrew HankinsonJohannes KepperKevin PageElisabete ShibataOne 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.https://journals.openedition.org/jtei/5832Music EncodingMetadataArrangementsData ModelingCorporaDigital Workflows
spellingShingle Mark Saccomano
Lisa Rosendahl
David Lewis
Andrew Hankinson
Johannes Kepper
Kevin Page
Elisabete Shibata
Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
Journal of the Text Encoding Initiative
Music Encoding
Metadata
Arrangements
Data Modeling
Corpora
Digital Workflows
title Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
title_full Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
title_fullStr Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
title_full_unstemmed Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
title_short Selective Encoding: Reducing the Burden of Transcription for Digital Musicologists
title_sort selective encoding reducing the burden of transcription for digital musicologists
topic Music Encoding
Metadata
Arrangements
Data Modeling
Corpora
Digital Workflows
url https://journals.openedition.org/jtei/5832
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