Optical Music Recognition of Printed White Mensural Notation: Conversion to Modern Notation Using Object Detection Mechanisms.
Today, the majority of music performers and vocalists are not able to read mensural notation fluently, and so conductors and music ensembles require modern editions for performing historical music. However, the conversion of printed white mensural sheet music into audible and performable modern nota...
| Publicado en: | International Journal of Humanities & Arts Computing: A Journal of Digital Humanities Vol. 16; no. 1; pp. 33 - 50 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Edinburgh University Press
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=155906998&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 155906998 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 17538548 2QD7 jtl: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities issn: 17538548 maglogo: N pubinfo: dt: Mar2022 vid: 16 iid: 1 pid: 2327 pub: Edinburgh University Press artinfo: ui: 155906998 10.3366/ijhac.2022.0275 ppf: 33 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Optical Music Recognition of Printed White Mensural Notation: Conversion to Modern Notation Using Object Detection Mechanisms. aug: au: Nowitzki, Olaf Engelhardt-Nowitzki, Corinna Fiala, Martin L. Wöber, Wilfried su: Object recognition (Computer vision) Sheet music Musical pitch Convolutional neural networks Conductors (Musicians) Ensemble music Music scores sug: subj: Object recognition (Computer vision) Sheet music Musical pitch Convolutional neural networks Conductors (Musicians) Ensemble music Music scores keyword: optical musical recognition white mensural notation ab: Today, the majority of music performers and vocalists are not able to read mensural notation fluently, and so conductors and music ensembles require modern editions for performing historical music. However, the conversion of printed white mensural sheet music into audible and performable modern notation currently requires elaborate manual editing by specialized music scholars. To close this gap, the present research proposes an algorithm that automatically converts scanned music score sheets of that historic period (the sixteenth and seventeenth centuries) into a file format that is readable in current notation software. This includes the optical recognition of musical symbols and respective semantic interpretation, for example note pitch determination. Based on works by the composers Sebastian Ertel and Paul Peuerl, the article presents a case study that combines convolutional neural networks with further computational process steps towards an integrated algorithm within a four-step optical music recognition (OMR) approach. As a result, the used musical material could be correctly converted into the MusicXML format with a recognition rate of 99 per cent. In the wake of these promising results, in future research we propose to extend our work to other materials, epochs, note symbols and advanced semantic analyses. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of International Journal of Humanities & Arts Computing: A Journal of Digital Humanities is the property of Edinburgh University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities holder: Edinburgh University Press dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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