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Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

机译:轻量级深度学习模型在高等院校声乐教育中的应用

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摘要

The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) method as the research object to explore the teaching reform of Music Major courses. Firstly, this article makes an in-depth study on Big Data Analytics (BDA) and Digital Twins (DTs) technology and constructs a DTs platform connecting real teaching space and virtual teaching space. Secondly, the DTs platform is divided into online learning feature analysis and virtualreal teaching space integration functional modules. This article explores the online immersive education process design and technology application of the DTs platform from the two aspects of teaching and technology. Afterward, it designs a student action and expression recognition network based on the Visual Geometry Group (VGG) Net model and Google Net model in teaching data collection and management. Finally, the proposed system is tested. The test results show that the active and passive interaction curves of the traditional VME system have no obvious fluctuation, indicating that the interaction of the traditional VME system is not strong, and the ability of active feedback information is poor. By contrast, the active and passive interaction curves in the proposed VME have large fluctuations, showing that the proposed VME has more frequent interaction, and the teaching information can get real-time and active feedback. Therefore, the proposed VME system can better stimulate students’ learning desire. Meanwhile, the constructed Neural Network (NN) has the highest recognition accuracy of 99.07 on the student action and expression dataset. When tested with the image data taken by the research experiment, the highest accuracy is 89, with an average of more than 85. The proposed VME system provides ideas for applying DTs technology in the college of music education.
机译:旨在提高音乐专业的教学质量,培养他们的创新能力。本文以声乐教育(VME)方法为研究对象,探讨音乐专业课程的教学改革。首先,本文对大数据分析(BDA)和数字孪生(DTs)技术进行了深入研究,构建了连接真实教学空间和虚拟教学空间的DTs平台。其次,将DTs平台分为在线学习特征分析和虚拟现实教学空间集成功能模块;本文从教学和技术两个方面探讨了DTs平台的在线沉浸式教育流程设计和技术应用。然后,基于视觉几何组(VGG)Net模型和Google Net模型设计了学生动作和表情识别网络,用于教学数据收集和管理。最后,对所提出的系统进行了测试。测试结果表明,传统VME系统的主动和被动交互曲线没有明显的波动,说明传统VME系统的交互性不强,主动反馈信息的能力较差。相比之下,所提出的VME中的主动和被动交互曲线波动较大,表明所提出的VME具有更频繁的交互,教学信息可以得到实时和主动的反馈。因此,所提出的VME系统可以更好地激发学生的学习欲望。同时,构建的神经网络(NN)在学生动作和表情数据集上的识别准确率最高,为99.07%。当使用研究实验拍摄的图像数据进行测试时,最高准确率为89%,平均超过85%。所提出的VME系统为DTs技术在音乐教育学院的应用提供了思路。

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