D. Griffiths, Stuart Cunningham, Jonathan Weinel, R. Picking
{"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}
引用次数: 8
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.
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.
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