Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation

Saari, Pasi; Fazekas, Gyorgy; Eerola, Tuomas; Barthet, Mathieu; Lartillot, Olivier; Sandler, Mark

Abstract

This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis.

Citation

Saari, P., Fazekas, G., Eerola, T., Barthet, M., Lartillot, O., & Sandler, M. (2016). Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation. IEEE Transactions on Affective Computing, 7(2), 122-135. https://doi.org/10.1109/taffc.2015.2462841

• Journal Article Type: Article
• Acceptance Date: Jul 28, 2015
• Online Publication Date: Jul 30, 2015
• Publication Date: May 26, 2016
• Deposit Date: Aug 18, 2015
• Publicly Available Date: Aug 24, 2015
• Journal: IEEE Transactions on Affective Computing
• Print ISSN: 1949-3045
• Electronic ISSN: 1949-3045
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 2
• Pages: 122-135
• DOI: https://doi.org/10.1109/taffc.2015.2462841
• Keywords: Music information retrieval, Mood prediction, Social tags, Semantic computing, Music genre, Genre-adaptive.
• Public URL: https://durham-repository.worktribe.com/output/1424031