Wearing sensor data integration for promoting the performance skills of music in IoT

Abstract

This study integrates multi-node wearable sensor data to improve music performance skills. A window-adding method is used during time-frequency feature extraction. By incorporating kernel functions, we present a generalized discriminant analysis (GDA) method to reduce the high-dimensional sensor features while retaining performance traits. Experiments demonstrate that the proposed GDA approach achieves higher accuracy (92.71%), precision (90.54%), and recall (88.68%) compared to linear discriminant analysis (82.39% accuracy) and principal component analysis (88.56% accuracy) in classifying motions performed by music performers. The integrated analysis of wearable sensor data facilitates comprehensive feedback to strengthen proficiency across various music performance skills.