2026-04-14T07:51:54Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/441302025-12-22T13:36:07Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Eremenko, Vsevolod author Morsi, Alia author Narang, Jyoti author Serra, Xavier author 2020-04-01T08:14:41Z 2020-04-01T08:14:41Z 2020 Comunicació presentada a: CSEDU 2020 The 12th International Conference on Computer Supported Education, celebrada del 2 al 4 de maig de 2020, en línia. 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. 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). Music education Music performance analysis Music assessment Audio signal processing Machine learning Music information retrieval Performance assessment technologies for the support of musical instrument learning