基于Leap motion传感器的音乐智能识别系统  

Music intelligent recognition system based on Leap motion sensor

作　　者：窦菲菲[1] 陈娟[1] DOU Feifei;CHEN Juan(Xianyang Normal University,Xianyang Shaanxi 712000,China)

机构地区：[1]咸阳师范学院,陕西咸阳712000

出　　处：《自动化与仪器仪表》2024年第7期302-306,共5页Automation & Instrumentation

基　　金：陕西省教育科学“十四五”规划2021年度课题《即兴伴奏分级教学研究》(2021SGH21Y0198)。

摘　　要：在智能技术不断发展的今天,音乐智能识别系统成为连接技术与艺术的重要桥梁。尤其是钢琴教学领域,由于其学习难度和高昂的教育成本,智能识别技术的应用显得尤为重要。为此,研究致力于开发一种基于Leap Motion传感器的音乐智能识别系统,旨在通过识别和分析钢琴专家的手势动作,为钢琴初学者提供一种更高效、更直观的学习方法。研究结合手指的生理结构和弹奏特性,建立了一个融合Denavit-Hartenberg参数的手指触键运动学模型,并使用Leap Motion采集的数据,通过反向神经网络对手指弹奏进行评估。结果显示,手指弹奏速度特征被成功捕捉;经特定去噪算法处理后,数据准确度达98%。而神经网络在测试样本上的误差仅为0.34%,显示出模型的高度精准性。该研究为音乐智能识别领域带来了重要的启示。In today’s continuous development of intelligent technology,music intelligent recognition system has become an important bridge connecting technology and art.Especially in the field of piano teaching,the application of intelligent recognition technology is particularly important due to its learning difficulty and high education cost.To this end,the research is dedicated to the development of a Leap Motion sensor-based music intelligent recognition system,which aims to provide a more efficient and intuitive learning method for piano beginners by recognising and analysing the gestural movements of piano experts.The study combines the physiological structure and playing characteristics of fingers,establishes a kinematic model of finger keystroke incorporating Denavit-Hartenberg parameters,and uses the data captured by Leap Motion to evaluate finger playing via inverse neural network.The results showed that finger playing velocity features were successfully captured;the data were processed by a specific denoising algorithm with 98%accuracy.And the error of the neural network on the test sample is only 0.34%,showing the high accuracy of the model.This study brings important insights to the field of music intelligent recognition.

关 键 词：音乐 Leap Motion Denavit-Hartenberg参数法 识别 

分 类 号：TP98[自动化与计算机技术]