Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

IF 3.1 Q1 Mathematics
Ni Zhang
{"title":"Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence","authors":"Ni Zhang","doi":"10.2478/amns.2023.2.01333","DOIUrl":null,"url":null,"abstract":"Abstract This paper utilizes deep learning algorithms to informally integrate modern vocal music teaching with traditional music culture and extracts audio time-domain features and frequency-domain features through neural network self-learning. Secondly, a large number of music tracks are decomposed into music patterns, which constitute a music pattern library, and a music training model is generated through the automatic music audio synthesis algorithm based on a recurrent neural network, and the GRU model is used for music training and model prediction. The strategy of integrating artificial intelligence and modern vocal music teaching mode through traditional music culture in modern vocal music teaching is informatized, and a controlled experiment is carried out with H Music Academy as an example. The results show that the average degree of completion of the learning objectives of the students in the two experimental classes is 89.32 and 87.16, respectively, which is 14.15 and 11.99 higher than the average degree of completion of the control class. This study demonstrates that the teaching mode of traditional music culture integration in modern vocal music teaching can enhance the student’s ability of vocal music skills and practically improve the students’ artistic literacy, which can improve the degree of completion of the student’s learning objectives and in turn, improve the overall level of vocal music teaching.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"81 19","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mathematics and Nonlinear Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/amns.2023.2.01333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract This paper utilizes deep learning algorithms to informally integrate modern vocal music teaching with traditional music culture and extracts audio time-domain features and frequency-domain features through neural network self-learning. Secondly, a large number of music tracks are decomposed into music patterns, which constitute a music pattern library, and a music training model is generated through the automatic music audio synthesis algorithm based on a recurrent neural network, and the GRU model is used for music training and model prediction. The strategy of integrating artificial intelligence and modern vocal music teaching mode through traditional music culture in modern vocal music teaching is informatized, and a controlled experiment is carried out with H Music Academy as an example. The results show that the average degree of completion of the learning objectives of the students in the two experimental classes is 89.32 and 87.16, respectively, which is 14.15 and 11.99 higher than the average degree of completion of the control class. This study demonstrates that the teaching mode of traditional music culture integration in modern vocal music teaching can enhance the student’s ability of vocal music skills and practically improve the students’ artistic literacy, which can improve the degree of completion of the student’s learning objectives and in turn, improve the overall level of vocal music teaching.
人工智能时代高校现代声乐教学与传统音乐文化的信息化融合策略
摘要本文利用深度学习算法将现代声乐教学与传统音乐文化进行非正式整合,通过神经网络自学习提取音频的时域特征和频域特征。其次,将大量的音乐曲目分解为音乐模式,构成音乐模式库,通过基于递归神经网络的音乐音频自动合成算法生成音乐训练模型,并利用GRU模型进行音乐训练和模型预测。对现代声乐教学中通过传统音乐文化将人工智能与现代声乐教学模式相结合的策略进行了信息化,并以H音乐学院为例进行了对照实验。结果表明,两个实验班学生的学习目标平均完成度分别为89.32和87.16,比对照班学生的平均完成度高14.15和11.99。本研究表明,在现代声乐教学中融入传统音乐文化的教学模式,可以增强学生的声乐技能能力,切实提高学生的艺术素养,从而提高学生学习目标的完成程度,进而提高声乐教学的整体水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信