High-Level Analysis of Audio Features for Identifying Emotional Valence in Human Singing

Conference paper


Cunningham, S, Weinel, J and Picking, R (2018). High-Level Analysis of Audio Features for Identifying Emotional Valence in Human Singing. Audio Mostly 2018: A conference on interaction with sound. Wrexham Glyndŵr University, North Wales, UK 12 - 14 Sep 2018 https://doi.org/10.1145/3243274.3243313
AuthorsCunningham, S, Weinel, J and Picking, R
TypeConference paper
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.

Year2018
JournalAM’18 Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion
Digital Object Identifier (DOI)https://doi.org/10.1145/3243274.3243313
Accepted author manuscript
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File Access Level
Open
Publication dates
Print12 Sep 2018
Publication process dates
Deposited05 Apr 2019
Accepted12 Sep 2018
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https://openresearch.lsbu.ac.uk/item/86965

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Accepted author manuscript
Cunningham_HighLevelAnalysis.pdf
License: CC BY 4.0
File access level: Open

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