dc.contributor.author
Eremenko, Vsevolod
dc.contributor.author
Morsi, Alia
dc.contributor.author
Narang, Jyoti
dc.contributor.author
Serra, Xavier
dc.date.issued
2020-04-01T08:14:41Z
dc.date.issued
2020-04-01T08:14:41Z
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
dc.format
application/pdf
dc.publisher
SCITEPRESS – Science and Technology Publications
dc.relation
info:eu-repo/grantAgreement/EC/H2020/768530
dc.rights
info:eu-repo/semantics/openAccess
dc.subject
Music education
dc.subject
Music performance analysis
dc.subject
Music assessment
dc.subject
Audio signal processing
dc.subject
Machine learning
dc.subject
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