Angelakis Evangelos, Bakogiannis Konstantinos, Georgaki Anastasia, Andreopoulou Areti
Laboratory of Music Acoustics and Technology (LabMAT), Department of Music Studies, National and Kapodistrian University of Athens, Panepistimioupoli, Ilisia, 15784 Athens, Greece.
Sensors (Basel). 2025 Jul 30;25(15):4713. doi: 10.3390/s25154713.
Operatic singing, traditionally taught through empirical and subjective methods, demands innovative approaches to enhance its pedagogical effectiveness today. This paper introduces a novel integration of advanced skeletal tracking technology into a prototype framework for operatic singing pedagogy research. Using the Microsoft Kinect Azure DK sensor, this prototype extracts detailed data on spinal, cervical, and shoulder alignment and movement data, with the aim of quantifying biomechanical movements during vocal performance. Preliminary results confirmed high face validity and biomechanical relevance. The incorporation of skeletal-tracking technology into vocal pedagogy research could help clarify certain technical aspects of singing and enhance sensorimotor feedback for the training of operatic singers.
传统上,歌剧演唱是通过经验和主观方法进行教学的,如今需要创新方法来提高其教学效果。本文介绍了一种将先进的骨骼跟踪技术新颖地集成到歌剧演唱教学研究的原型框架中的方法。使用微软Kinect Azure DK传感器,该原型提取有关脊柱、颈椎和肩部对齐以及运动数据的详细数据,目的是量化声乐表演期间的生物力学运动。初步结果证实了较高的表面效度和生物力学相关性。将骨骼跟踪技术纳入声乐教学研究有助于阐明演唱的某些技术方面,并增强对歌剧演唱者训练的感觉运动反馈。