Giraldo Sergio, Waddell George, Nou Ignasi, Ortega Ariadna, Mayor Oscar, Perez Alfonso, Williamon Aaron, Ramirez Rafael
Music Technology Group, Music and Machine Learning Lab, Department of Communications and Technology, Pompeu Fabra University, Barcelona, Spain.
Centre for Performance Science, Royal College of Music, London, United Kingdom.
Front Psychol. 2019 Mar 14;10:334. doi: 10.3389/fpsyg.2019.00334. eCollection 2019.
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.
由于技术增强型音乐学习系统的数量不断增加，音乐表演的自动评估已成为一个越来越受关注的领域。在大多数这些系统中，音乐表演的评估基于音高和起始准确性，但很少有系统关注表演的其他重要方面，如音质或音色。在小提琴教育中尤其如此，在小提琴教育中，音色质量在音乐表演评估中起着重要作用。然而，获得用于评估音色质量的可量化标准具有挑战性，因为它依赖于专家主观解释之间的共识。我们提出了一种使用机器学习技术评估小提琴表演中音色质量的方法。我们收集了几种音质的音频记录，并进行了感知测试，以找到不同音色维度之间的相关性。我们对音频记录进行处理，以提取用于训练音质模型的声学特征。分析了提取特征之间的相关性，并研究了用于区分不同音色质量的特征信息。我们实现了一个为教学用途设计的实时反馈系统，用户可以在该系统中训练自己的音色模型，以评估自己的表演并获得反馈。
