e-space
Manchester Metropolitan University's Research Repository

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

    Griffiths, Darryl, Cunningham, Stuart ORCID logoORCID: https://orcid.org/0000-0002-5348-7700, Weinel, Jonathan and Picking, Richard (2021) A multi-genre model for music emotion recognition using linear regressors. Journal of New Music Research, 50 (4). pp. 355-372. ISSN 0929-8215

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution Non-commercial No Derivatives.

    Download (2MB) | Preview

    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.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    380Downloads
    6 month trend
    135Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record