首页> 外文期刊>Mobile information systems >Multimedia Teaching of College Musical Education Based on Deep Learning
【24h】

Multimedia Teaching of College Musical Education Based on Deep Learning

机译:基于深度学习的大学音乐教育多媒体教学

获取原文
       

摘要

In view of the current situation of musical education and the need for reform in China, we adopt two different methods, i.e., literature method and interview method in this research work. From these methods, we read a lot of musical education, multimedia technology, and modern teaching and reform. This research work is divided into two main phases. Firstly, the article mainly discusses the characteristics of college musical education compared with other cultural courses and the feasibility of multimedia technology and the auxiliary function of musical education that is applied in school’s musical education. Secondly, brain computing attempts to analyze things by simulating the structure and information processing of biological neural networks. The intelligent learning characteristic of a deep learning algorithm is proposed to monitor the process of musical education teaching and analyze the process quality. Finally, we introduced the design and production of network multimedia courseware which will help in theoretical guidance and reference to the application of multimedia technology in college musical education in China. Moreover, the outcome of the proposed model can play a role in solving and answering questions in the current multimedia application process and Chinese college music workers will apply multimedia technology more effectively and skillfully.
机译:鉴于目前的音乐教育情况和中国改革的需求,我们采用了两种不同的方法,即文学方法和面试方法在本研究工作中。从这些方法中,我们阅读了很多音乐教育,多媒体技术和现代教学和改革。这项研究工作分为两个主要阶段。首先,本文主要讨论了大学音乐教育的特点与其他文化课程相比和多媒体技术的可行性以及在学校音乐教育中应用的音乐教育的辅助功能。其次,脑计算尝试通过模拟生物神经网络的结构和信息处理来分析事物。建议深入学习算法的智能学习特征,监测音乐教育教学过程,分析过程质量。最后,我们介绍了网络多媒体课件的设计和制作,有助于理论指导,并参考多媒体技术在中国大学音乐教育中的应用。此外,拟议模型的结果可以在当前的多媒体应用程序过程中解决和回答问题中的角色,而中国大学音乐工作人员将更有效地应用多媒体技术。

著录项

  • 来源
    《Mobile information systems》 |2021年第a期|共10页
  • 作者

    Wei Li;

  • 作者单位
  • 收录信息
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 关键词

  • 入库时间 2022-08-19 02:20:23

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号