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

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

Griffiths, Darryl, Cunningham, Stuart, Weinel, Jonathan ORCID: 0000-0001-5347-3897 and Picking, Richard (2021) A multi-genre model for music emotion recognition using linear regressors. Journal of New Music Research. ISSN 0929-8215 (Print), 1744-5027 (Online) (doi:https://doi.org/10.1080/09298215.2021.1977336)

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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.

Item Type: Article
Uncontrolled Keywords: arousal, emotion, MER, music perception, regression, valence
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CMS)
Faculty of Liberal Arts & Sciences > Sound-Image Research Group
Related URLs:
Last Modified: 21 Oct 2021 10:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/34053

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