Analysis of the application of HMM algorithm in teaching musical note feature recognition in universities

Q4 Decision Sciences
Fei Huang, Meiqun Liao
{"title":"Analysis of the application of HMM algorithm in teaching musical note feature recognition in universities","authors":"Fei Huang, Meiqun Liao","doi":"10.1504/ijnvo.2023.133868","DOIUrl":null,"url":null,"abstract":"With the rapid development of music education and information technology in colleges and universities, how to improve the efficiency of teachers' teaching in current music courses has increasingly become a focus of public attention. This study aims to propose an HMM algorithm based on the application of music note feature recognition teaching in colleges and universities. The experimental results show that the HMM algorithm is used in the music frequency sample signal after pre-processing, and its target accuracy is reached after 20 training sessions. Comparing the HMM algorithm with the other two algorithms, the results show that its correct rate is about 99.56%, and the probability of occurrence of insertion error and elimination error is 0.52% and 2.58%, which is better than the other two algorithms. In summary, it shows that the research proposed HMM algorithm has some practical value and relevance to the teaching of music in colleges and universities.","PeriodicalId":52509,"journal":{"name":"International Journal of Networking and Virtual Organisations","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Networking and Virtual Organisations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijnvo.2023.133868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

With the rapid development of music education and information technology in colleges and universities, how to improve the efficiency of teachers' teaching in current music courses has increasingly become a focus of public attention. This study aims to propose an HMM algorithm based on the application of music note feature recognition teaching in colleges and universities. The experimental results show that the HMM algorithm is used in the music frequency sample signal after pre-processing, and its target accuracy is reached after 20 training sessions. Comparing the HMM algorithm with the other two algorithms, the results show that its correct rate is about 99.56%, and the probability of occurrence of insertion error and elimination error is 0.52% and 2.58%, which is better than the other two algorithms. In summary, it shows that the research proposed HMM algorithm has some practical value and relevance to the teaching of music in colleges and universities.
HMM算法在高校音符特征识别教学中的应用分析
随着高校音乐教育和信息技术的快速发展,如何提高当前音乐课程教师的教学效率日益成为人们关注的焦点。本研究旨在提出一种基于HMM算法在高校音符特征识别教学中的应用。实验结果表明,将HMM算法应用于预处理后的音乐频率样本信号中,经过20次训练后达到了目标准确率。将HMM算法与其他两种算法进行比较,结果表明其正确率约为99.56%,插入错误和消除错误的发生概率分别为0.52%和2.58%,优于其他两种算法。综上所述,研究提出的HMM算法对高校音乐教学具有一定的实用价值和相关性。
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
International Journal of Networking and Virtual Organisations
International Journal of Networking and Virtual Organisations Decision Sciences-Information Systems and Management
CiteScore
1.40
自引率
0.00%
发文量
25
期刊介绍: IJNVO is a forum aimed at providing an authoritative refereed source of information in the field of Networking and Virtual Organisations.
×
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