Authors:
Vsevolod Eremenko
;
Alia Morsi
;
Jyoti Narang
and
Xavier Serra
Affiliation:
Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
Keyword(s):
Music Education, Music Performance Analysis, Music Assessment, Audio Signal Processing, Machine Learning, Music Information Retrieval.
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 supp
orting 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.
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