ArticleBehavior research methods2022
Audience facial expressions detected by automated face analysis software reflect emotions in music.
Article in Behavior research methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Pooled it
- TraceLAB: A MATLAB Toolbox for Interindividual Synchrony Analysis of Facial Expression and Head Movement Data Acquired via Trace.Entropy (Basel, Switzerland) · 2026Article
- Facial Emotional Expression in Reaction to Internet Gaming Videos Among Young Adults: A Preliminary and Exploratory Study.Neuropsychopharmacology reports · 2025Article
- Article
- Inter-subject correlation of audience facial expressions predicts audience engagement during theatrical performances.iScience · 2024Article
- Age and familiarity effects on musical memory.PloS one · 2024Article
- Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An abundance of studies on emotional experiences in response to music have been published over the past decades, however, most have been carried out in controlled laboratory settings and rely on subjective reports. Facial expressions have been occasionally assessed but measured using intrusive methods such as facial electromyography (fEMG). The present study investigated emotional experiences of fifty participants in a live concert. Our aims were to explore whether automated face analysis could detect facial expressions of emotion in a group of people in an ecologically valid listening context, to determine whether emotions expressed by the music predicted specific facial expressions and examine whether facial expressions of emotion could be used to predict subjective ratings of pleasantness and activation. During the concert, participants were filmed and facial expressions were subsequently analyzed with automated face analysis software. Self-report on participants' subjective experience of pleasantness and activation were collected after the concert for all pieces (two happy, two sad). Our results show that the pieces that expressed sadness resulted in more facial expressions of sadness (compared to happiness), whereas the pieces that expressed happiness resulted in more facial expressions of happiness (compared to sadness). Differences for other facial expression categories (anger, fear, surprise, disgust, and neutral) were not found. Independent of the musical piece or emotion expressed in the music facial expressions of happiness predicted ratings of subjectively felt pleasantness, whilst facial expressions of sadness and disgust predicted low and high ratings of subjectively felt activation, respectively. Together, our results show that non-invasive measurements of audience facial expressions in a naturalistic concert setting are indicative of emotions expressed by the music, and the subjective experiences of the audience members themselves.
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