Towards Musicologist-driven Mining of Handwritten

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aut.relation.endpage6
aut.relation.journalIEEE Intelligent Systemsen_NZ
aut.relation.pages6
aut.relation.startpage1
aut.researcherYan, Wei-Qi
dc.contributor.authorNiitsuma, Men_NZ
dc.contributor.authorYan, W-Qen_NZ
dc.contributor.authorTomita, Yen_NZ
dc.contributor.authorBell, Den_NZ
dc.date.accessioned2019-03-04T22:50:38Z
dc.date.available2019-03-04T22:50:38Z
dc.date.copyright2017-12-31en_NZ
dc.date.issued2017-12-31en_NZ
dc.description.abstractHistorical musicologists have been seeking for objective and powerful techniques to collect, analyse and verify their findings for many decades. The aim of this study was to show the importance of such domain-specific problems to achieve actionable knowledge discovery in the real the world. Our focus is on finding evidence for the chronological ordering of J.S. Bach’s manuscripts, by proposing a musicologist-driven mining method for extracting quantitative information from early music manuscripts. Bach’s C- clefs were extracted from a wide range of manuscripts under the direction of domain experts, and with these the classification of C-clefs was conducted. The proposed methods were evaluated on a dataset containing over 1000 clefs extracted from J.S. Bach’s manuscripts. The results show more than 70% accuracy for dating J.S. Bach’s manuscripts. Dating of Bach’s lost manuscripts was quantitatively hypothesized, providing a rough barometer to be combined with other evidence to evaluate musicologists’ hypotheses, and the practicability of this domain-driven approach is demonstrated.en_NZ
dc.identifier.citationIEEE Intelligent Systems, vol. 33, no. 4, pp. 24-34, Jul./Aug. 2018. doi: 10.1109/MIS.2018.111144115
dc.identifier.doi10.1109/MIS.2018.111144115en_NZ
dc.identifier.issn1541-1672en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/12306
dc.languageEnglishen_NZ
dc.publisherInstitute of Electrical and Electronics Engineersen_NZ
dc.relation.urihttps://www.computer.org/csdl/magazine/ex/2018/04/mex2018040024/17D45WYQJ90en_NZ
dc.rightsCopyright © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectDomain-driven data mining; Optical music recognition; Historical musicology; Music informatics; Music information retrieval
dc.titleTowards Musicologist-driven Mining of Handwrittenen_NZ
dc.typeJournal Article
pubs.elements-id284503
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
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