Abstract
The automatic assessment of music performance has become an area of special interest due to the increasing amount of technology-enhanced music learning systems. However, in most of these systems the assessment of the musical performance is based on the accuracy of onsets and pitch, paying little attention to other relevant aspects of performance. In this paper we present a preliminary study to assess the quality of violin performance using machine learning techniques. We collect recording examples of selected violin exercises varying from expert to amateur performances. We process the audio signal to extract features to train models using clustering based on Dynamic Time Warping distance. The quality of new performances is evaluated based on the level of match/miss-match to each of the recorded training examples.
Highlights
The qualitative assessment of music performance is an essential task in music education
In this paper we present a preliminary study for the automatic assessment of music performance, using Dynamic Time Warping Classification [10]
In this study we have presented a framework to automatically assess music performance in violin
Summary
The qualitative assessment of music performance is an essential task in music education. Music information retrieval technologies can play an important role in music education [1]. An important amount of systems are being developed to enhance the learning process of musical instruments, which make use of audio signal processing technologies for music information retrieval [1], some of them providing automatic assessment tools. Song2see [4] is an application gaming software for music learning and practicing that make use of Music Information Retrieval tools such as pitch detection and source separation to return feedback based on a rating system. Others commercial systems such as Yousician and Smart Music, are able to provide realtime feedback rating of music performance.
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