Retrieving Music Semantics from Optical Music Recognition by Machine Translation
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http://hdl.handle.net/10045/109930
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Camp Dublin Core | Valor | Idioma |
---|---|---|
dc.contributor | Reconocimiento de Formas e Inteligencia Artificial | es_ES |
dc.contributor.author | Thomae, Martha E. | - |
dc.contributor.author | Ríos-Vila, Antonio | - |
dc.contributor.author | Calvo-Zaragoza, Jorge | - |
dc.contributor.author | Rizo, David | - |
dc.contributor.author | Iñesta, José M. | - |
dc.contributor.other | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | es_ES |
dc.date.accessioned | 2020-10-26T11:44:41Z | - |
dc.date.available | 2020-10-26T11:44:41Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Thomae, Martha E., et al. “Retrieving Music Semantics from Optical Music Recognition by Machine Translation”. In: De Luca, Elsa; Flanders, Julia (Eds.). Music Encoding Conference Proceedings 26-29 May, 2020 Tufts University, Boston (USA), pp. 19-24. https://doi.org/10.17613/605z-nt78 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10045/109930 | - |
dc.description.abstract | In this paper, we apply machine translation techniques to solve one of the central problems in the field of optical music recognition: extracting the semantics of a sequence of music characters. So far, this problem has been approached through heuristics and grammars, which are not generalizable solutions. We borrowed the seq2seq model and the attention mechanism from machine translation to address this issue. Given its example-based learning, the model proposed is meant to apply to different notations provided there is enough training data. The model was tested on the PrIMuS dataset of common Western music notation incipits. Its performance was satisfactory for the vast majority of examples, flawlessly extracting the musical meaning of 85% of the incipits in the test set—mapping correctly series of accidentals into key signatures, pairs of digits into time signatures, combinations of digits and rests into multi-measure rests, detecting implicit accidentals, etc. | es_ES |
dc.description.sponsorship | This work is supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU, and by CIRMMT’s Inter-Centre Research Exchange Funding and McGill’s Graduate Mobility Award. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Tufts University | es_ES |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives License | es_ES |
dc.subject | Music semantics | es_ES |
dc.subject | Optical music recognition | es_ES |
dc.subject | Machine translation | es_ES |
dc.subject.other | Lenguajes y Sistemas Informáticos | es_ES |
dc.title | Retrieving Music Semantics from Optical Music Recognition by Machine Translation | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.17613/605z-nt78 | - |
dc.relation.publisherversion | https://doi.org/10.17613/605z-nt78 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86576-R | - |
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Thomae_etal_2020_Music_encoding_conference_proceedings.pdf | 545,63 kB | Adobe PDF | Obrir Vista prèvia | |
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