基于线性回归的音乐情感识别多流派模型

IF 1.1 4区计算机科学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of New Music Research Pub Date : 2021-08-08 DOI:10.1080/09298215.2021.1977336

D. Griffiths, Stuart Cunningham, Jonathan Weinel, R. Picking

{"title":"基于线性回归的音乐情感识别多流派模型","authors":"D. Griffiths, Stuart Cunningham, Jonathan Weinel, R. Picking","doi":"10.1080/09298215.2021.1977336","DOIUrl":null,"url":null,"abstract":"ABSTRACT Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence.","PeriodicalId":16553,"journal":{"name":"Journal of New Music Research","volume":"50 1","pages":"355 - 372"},"PeriodicalIF":1.1000,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A multi-genre model for music emotion recognition using linear regressors\",\"authors\":\"D. Griffiths, Stuart Cunningham, Jonathan Weinel, R. Picking\",\"doi\":\"10.1080/09298215.2021.1977336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence.\",\"PeriodicalId\":16553,\"journal\":{\"name\":\"Journal of New Music Research\",\"volume\":\"50 1\",\"pages\":\"355 - 372\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2021-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of New Music Research\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/09298215.2021.1977336\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of New Music Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/09298215.2021.1977336","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 8

摘要

将人类情感与音乐联系起来是一项挑战。我们的目标是创造一个有效的系统，能够从情感上评价多种类型的歌曲。为了实现这一目标，我们采用了一系列在线自我报告研究，利用罗素的循环模型。第一项研究(n = 44)确定了20首歌曲的唤醒和效价的音频特征。由此，我们构造了一组线性回归量。第二项研究(n = 158)测量了我们的系统的有效性，利用40首新歌来创造一个基本的事实。结果表明，我们的方法在情感评价音乐方面可能是有效的，特别是在预测效价方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A multi-genre model for music emotion recognition using linear regressors

ABSTRACT Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence.

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

Journal of New Music Research 工程技术-计算机：跨学科应用

CiteScore

3.20

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of New Music Research (JNMR) publishes material which increases our understanding of music and musical processes by systematic, scientific and technological means. Research published in the journal is innovative, empirically grounded and often, but not exclusively, uses quantitative methods. Articles are both musically relevant and scientifically rigorous, giving full technical details. No bounds are placed on the music or musical behaviours at issue: popular music, music of diverse cultures and the canon of western classical music are all within the Journal’s scope. Articles deal with theory, analysis, composition, performance, uses of music, instruments and other music technologies. The Journal was founded in 1972 with the original title Interface to reflect its interdisciplinary nature, drawing on musicology (including music theory), computer science, psychology, acoustics, philosophy, and other disciplines.