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High-level analysis of audio features for identifying emotional valence in human singing

High-level analysis of audio features for identifying emotional valence in human singing

Cunningham, Stuart, Weinel, Jonathan ORCID: 0000-0001-5347-3897 and Picking, Richard (2018) High-level analysis of audio features for identifying emotional valence in human singing. In: AM'18: Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion. September 12-14 2018, Wrexham, Wales UK. Association for Computing Machinery (ACM), New York, pp. 1-4. ISBN 978-1-4503-6609-0 ; 978-1-4503-6609-0/18/09 (doi:https://doi.org/10.1145/3243274.3243313)

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Abstract

Emotional analysis continues to be a topic that receives much attention in the audio and music community. The potential to link together human affective state and the emotional content or intention of musical audio has a variety of application areas in fields such as improving user experience of digital music libraries and music therapy. Less work has been directed into the emotional analysis of human acapella singing. Recently, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) was released, which includes emotionally validated human singing samples. In this work, we apply established audio analysis features to determine if these can be used to detect underlying emotional valence in human singing. Results indicate that the short-term audio features of: energy; spectral centroid (mean); spectral centroid (spread); spectral entropy; spectral flux; spectral rolloff; and fundamental frequency can be useful predictors of emotion, although their efficacy is not consistent across positive and negative emotions

Item Type: Conference Proceedings
Title of Proceedings: AM'18: Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion. September 12-14 2018, Wrexham, Wales UK
Uncontrolled Keywords: audio analysis, music information retrieval
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 10:27
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/34072

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