Performance assessment technologies for the support of musical instrument learning

dc.contributor.author
Eremenko, Vsevolod
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Morsi, Alia
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Narang, Jyoti
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Serra, Xavier
dc.date.issued
2020-04-01T08:14:41Z
dc.date.issued
2020-04-01T08:14:41Z
dc.date.issued
2020
dc.identifier
Eremenko V, Morsi A, Narang J, Serra X. Performance assessment technologies for the support of musical instrument learning. Paper presented at: CSEDU 2020 The 12th International Conference on Computer Supported Education; 2020 May 2-4.
dc.identifier
http://hdl.handle.net/10230/44130
dc.description.abstract
Comunicació presentada a: CSEDU 2020 The 12th International Conference on Computer Supported Education, celebrada del 2 al 4 de maig de 2020, en línia.
dc.description.abstract
Recent technological developments are having a significant impact on musical instruments and singing voice learning. A proof is the number of successful software applications that are being used by aspiring musicians in their regular practice. These practicing apps offer many useful functionalities to support learning, including performance assessment technologies that analyze the sound produced by the student while playing, identifying performance errors and giving useful feedback. However, despite the advancements in these sound analysis technologies, they are still not reliable and effective enough to support the strict requirements of a professional music education context. In this article we first introduce the topic and context, reviewing some of the work done in the practice of music assessment, then going over the current state of the art in performance assessment technologies, and presenting, as a proof of concept, a complete assessment system that we have developed for supporting guitar exercises. We conclude by identifying the challenges that should be addressed in order to further advance these assessment technologies and their useful integration into professional learning contexts.
dc.description.abstract
This research was partly funded by the European Research Council under the European Union’s Seventh Framework Program, as part of the TECSOME project (ERC grant agreement 768530).
dc.format
application/pdf
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application/pdf
dc.language
eng
dc.publisher
SCITEPRESS – Science and Technology Publications
dc.relation
info:eu-repo/grantAgreement/EC/H2020/768530
dc.rights
© SCITEPRESS
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info:eu-repo/semantics/openAccess
dc.subject
Music education
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Music performance analysis
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Music assessment
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Audio signal processing
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Machine learning
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Music information retrieval
dc.title
Performance assessment technologies for the support of musical instrument learning
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:eu-repo/semantics/acceptedVersion


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