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小提琴运弓技巧学习的实时声音与动作反馈:一项对照随机试验。

Real-Time Sound and Motion Feedback for Violin Bow Technique Learning: A Controlled, Randomized Trial.

作者信息

Blanco Angel David, Tassani Simone, Ramirez Rafael

机构信息

Music and Machine Learning Lab, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Multiscale and Computational Biomechanics and Mechanobiology Team, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

出版信息

Front Psychol. 2021 Apr 26;12:648479. doi: 10.3389/fpsyg.2021.648479. eCollection 2021.

Abstract

The production of good sound generation in the violin is a complex task that requires coordination and spatiotemporal control of bowing gestures. The use of motion-capture technologies to improve performance or reduce injury risks in the area of kinesiology is becoming widespread. The combination of motion accuracy and sound quality feedback has the potential of becoming an important aid in violin learning. In this study, we evaluate motion-capture and sound-quality analysis technologies developed inside the context of the TELMI, a technology-enhanced music learning project. We analyzed the sound and bow motion of 50 participants with no prior violin experience while learning to produce a stable sound in the violin. Participants were divided into two groups: the experimental group ( = 24) received real-time visual feedback both on kinematics and sound quality, while participants in the control group ( = 26) practiced without any type of external help. An additional third group of violin experts performed the same task for comparative purposes ( = 15). After the practice session, all groups were evaluated in a transfer phase without feedback. At the practice phase, the experimental group improved their bowing kinematics in comparison to the control group, but this was at the expense of impairing the sound quality of their performance. At the retention phase, the experimental group showed better results in sound quality, especially concerning control of sound dynamics. Besides, we found that the expert group improved the stability of their sound while using the technology. All in all, these results emphasize the importance of feedback technologies in learning complex tasks, such as musical instrument learning.

摘要

在小提琴上产生良好的发声效果是一项复杂的任务,需要协调和对运弓动作进行时空控制。在运动机能学领域,使用动作捕捉技术来提高演奏水平或降低受伤风险正变得越来越普遍。动作准确性和音质反馈的结合有可能成为小提琴学习中的一项重要辅助手段。在本研究中,我们评估了在TELMI(一个技术增强型音乐学习项目)背景下开发的动作捕捉和音质分析技术。我们分析了50名此前没有小提琴经验的参与者在学习在小提琴上发出稳定声音时的声音和运弓动作。参与者被分为两组:实验组(n = 24)同时获得了关于运动学和音质的实时视觉反馈,而对照组(n = 26)在没有任何外部帮助的情况下进行练习。另外还有一组小提琴专家为了比较目的执行相同任务(n = 15)。在练习阶段之后,所有组在没有反馈的迁移阶段接受评估。在练习阶段,与对照组相比,实验组改善了他们的运弓运动学,但这是以牺牲其演奏音质为代价的。在保留阶段,实验组在音质方面表现出更好的结果,尤其是在声音动态控制方面。此外,我们发现专家组在使用该技术时提高了他们声音的稳定性。总而言之,这些结果强调了反馈技术在学习复杂任务(如乐器学习)中的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe60/8107276/dcb8a3eb4ded/fpsyg-12-648479-g0001.jpg

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