期刊
FRONTIERS IN PSYCHOLOGY
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fpsyg.2019.00334
关键词
automatic assessment of music; machine learning; violin performance; tone quality; music performance
资金
- Spanish TIN project TIMUL [TIN2013-48152-C2-2-R]
- European Union Horizon 2020 research and innovation programme [688269]
- Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme [MDM-2015-0502]
The automatic assessment of music performance has become an area of increasing interest due to the growing number of technology-enhanced music learning systems. In most of these systems, the assessment of musical performance is based on pitch and onset accuracy, but very few pay attention to other important aspects of performance, such as sound quality or timbre. This is particularly true in violin education, where the quality of timbre plays a significant role in the assessment of musical performances. However, obtaining quantifiable criteria for the assessment of timbre quality is challenging, as it relies on consensus among the subjective interpretations of experts. We present an approach to assess the quality of timbre in violin performances using machine learning techniques. We collected audio recordings of several tone qualities and performed perceptual tests to find correlations among different timbre dimensions. We processed the audio recordings to extract acoustic features for training tone-quality models. Correlations among the extracted features were analyzed and feature information for discriminating different timbre qualities were investigated. A real-time feedback system designed for pedagogical use was implemented in which users can train their own timbre models to assess and receive feedback on their performances.
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