{"title":"基于新黎曼理论和贝叶斯网络的音乐转换研究","authors":"Takuto Machida, A. Ito, Koji Mikami","doi":"10.1109/NicoInt55861.2022.00032","DOIUrl":null,"url":null,"abstract":"In this study, two theoretical studies were combined. On the music theory side, we used Neo-Riemannian theory to analyze the music, and Bayesian networks on the mathematics side. R was used to construct the Bayesian network. Using these two methods, we have developed a mathematical model of musical transitions, and have also attempted to create music based on the probability of chord progressions.","PeriodicalId":328114,"journal":{"name":"2022 Nicograph International (NicoInt)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Music Transition Using Neo- Riemannian Theory and Bayesian Network\",\"authors\":\"Takuto Machida, A. Ito, Koji Mikami\",\"doi\":\"10.1109/NicoInt55861.2022.00032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, two theoretical studies were combined. On the music theory side, we used Neo-Riemannian theory to analyze the music, and Bayesian networks on the mathematics side. R was used to construct the Bayesian network. Using these two methods, we have developed a mathematical model of musical transitions, and have also attempted to create music based on the probability of chord progressions.\",\"PeriodicalId\":328114,\"journal\":{\"name\":\"2022 Nicograph International (NicoInt)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Nicograph International (NicoInt)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NicoInt55861.2022.00032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Nicograph International (NicoInt)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NicoInt55861.2022.00032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Music Transition Using Neo- Riemannian Theory and Bayesian Network
In this study, two theoretical studies were combined. On the music theory side, we used Neo-Riemannian theory to analyze the music, and Bayesian networks on the mathematics side. R was used to construct the Bayesian network. Using these two methods, we have developed a mathematical model of musical transitions, and have also attempted to create music based on the probability of chord progressions.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
