Trajectory of Fifths Based on Chroma Subbands Extraction–A New Approach to Music Representation, Analysis, and Classification

Tomasz Lukaszewicz;Dariusz Kania
{"title":"Trajectory of Fifths Based on Chroma Subbands Extraction–A New Approach to Music Representation, Analysis, and Classification","authors":"Tomasz Lukaszewicz;Dariusz Kania","doi":"10.1109/TPAMI.2024.3519420","DOIUrl":null,"url":null,"abstract":"In this article, we propose a new method of representing and analyzing music audio records. The method is based on the concept of the trajectory of fifths, which was initially developed for the analysis of music represented in MIDI format. To adapt this concept to the needs of audio signal processing, we implement a short-term spectral analysis of a musical piece, followed by a mapping of its subsequent spectral timeframes onto signatures of fifths reflecting relative intensities of sounds associated with each of the 12 pitch classes. Subsequently, the calculation of the characteristic points of the consecutive signatures of fifths enables the creation of the trajectory of fifths. The results of the experiments and statistical analysis conducted in a set of 8996 audio music pieces belonging to 10 genres indicate that this kind of trajectory, just as its MIDI-compliant precursor, is a source of valuable information (i.e., feature coefficients) concerning the harmonic structure of music, which may find use in audio music classification processes.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"47 3","pages":"2157-2169"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804652","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on pattern analysis and machine intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10804652/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this article, we propose a new method of representing and analyzing music audio records. The method is based on the concept of the trajectory of fifths, which was initially developed for the analysis of music represented in MIDI format. To adapt this concept to the needs of audio signal processing, we implement a short-term spectral analysis of a musical piece, followed by a mapping of its subsequent spectral timeframes onto signatures of fifths reflecting relative intensities of sounds associated with each of the 12 pitch classes. Subsequently, the calculation of the characteristic points of the consecutive signatures of fifths enables the creation of the trajectory of fifths. The results of the experiments and statistical analysis conducted in a set of 8996 audio music pieces belonging to 10 genres indicate that this kind of trajectory, just as its MIDI-compliant precursor, is a source of valuable information (i.e., feature coefficients) concerning the harmonic structure of music, which may find use in audio music classification processes.
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
基于色度子带提取的五度轨迹--音乐表现、分析和分类的新方法
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fully-Connected Transformer for Multi-Source Image Fusion RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model Natural Adversarial Mask for Face Identity Protection in Physical World Multi-Head Encoding for Extreme Label Classification Hierarchical Banzhaf Interaction for General Video-Language Representation Learning
×
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学术文献互助
Book学术文献互助群
群 号:481959085
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