基于机器学习和人工智能的乐谱自动生成算法

Ruize Yu, Yu Sun
{"title":"基于机器学习和人工智能的乐谱自动生成算法","authors":"Ruize Yu, Yu Sun","doi":"10.5121/csit.2022.121822","DOIUrl":null,"url":null,"abstract":"Due to the ever growing popularity of music as a part of everyday life, and with the continuous advances in AI technology, it is now possible for computers to listen to and recognize music [1]. However, there still exist limitations on machines’ ability to recognize audio. This paper proposes an application to simplify the process of music transcription and reduce its runtime [2]. This application was tested in a different range of settings and evaluated. The results show what can be further improved on this application.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"56 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Automatic Sheet Music Generating Algorithm based on Machine Learning and Artificial Intelligence\",\"authors\":\"Ruize Yu, Yu Sun\",\"doi\":\"10.5121/csit.2022.121822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the ever growing popularity of music as a part of everyday life, and with the continuous advances in AI technology, it is now possible for computers to listen to and recognize music [1]. However, there still exist limitations on machines’ ability to recognize audio. This paper proposes an application to simplify the process of music transcription and reduce its runtime [2]. This application was tested in a different range of settings and evaluated. The results show what can be further improved on this application.\",\"PeriodicalId\":91205,\"journal\":{\"name\":\"Artificial intelligence and applications (Commerce, Calif.)\",\"volume\":\"56 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial intelligence and applications (Commerce, Calif.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/csit.2022.121822\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence and applications (Commerce, Calif.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/csit.2022.121822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

由于音乐作为日常生活的一部分越来越受欢迎,并且随着人工智能技术的不断进步,现在计算机可以听音乐并识别音乐[1]。然而,机器识别音频的能力仍然存在局限性。本文提出了一种简化音乐转录过程并缩短其运行时间的应用[2]。该应用程序在不同的设置范围内进行了测试和评估。结果显示了在这个应用程序上可以进一步改进的地方。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Automatic Sheet Music Generating Algorithm based on Machine Learning and Artificial Intelligence
Due to the ever growing popularity of music as a part of everyday life, and with the continuous advances in AI technology, it is now possible for computers to listen to and recognize music [1]. However, there still exist limitations on machines’ ability to recognize audio. This paper proposes an application to simplify the process of music transcription and reduce its runtime [2]. This application was tested in a different range of settings and evaluated. The results show what can be further improved on this application.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review. Methodology of Measurement Intellectualization based on Regularized Bayesian Approach in Uncertain Conditions Stochastic Dual Coordinate Ascent for Learning Sign Constrained Linear Predictors Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification Batch-Stochastic Sub-Gradient Method for Solving Non-Smooth Convex Loss Function Problems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1