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

Jonathan Weinel

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - 12 Sept 2018
Externally publishedYes
EventAudio Mostly 2018: A conference on interaction with sound -
Duration: 9 Dec 2018 → …

Conference

ConferenceAudio Mostly 2018: A conference on interaction with sound
Period9/12/18 → …

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