A study on the emotional accuracy of generative AI music from the perspective of the “Emotion Wheel” theory

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Human-composed music showed significantly higher emotional accuracy than Suno AI for rage, ecstasy and terror, but not for grief.
Largest accuracy gaps: rage 25.40%, ecstasy 21.50%, terror 17.00%; grief difference 4.20% was not statistically significant.
Findings indicate current generative models struggle with high-arousal emotional expression, suggesting need for improved conditioning and more diverse training data.

AI Summary

This study examines differences in emotional expression accuracy between AI-generated music and compositions by traditional musicians, with a particular focus on Suno AI within the theoretical framework of Plutchik's "Wheel of Emotions." Emotional expression accuracy is defined as the degree of correspondence between the intended target emotion of a musical piece and the emotion perceived by listeners.

RESULTS: The results revealed that emotional expression accuracy differed significantly between human-composed and AI-generated music for rage (χ2 = 40.66, p < 0.001, Cramer's V = 0.27), ecstasy (χ2 = 25.53, p < 0.001, V = 0.21), and terror (χ2 = 16.46, p < 0.001, V = 0.17), whereas no significant difference was observed for grief (χ2 = 2.44, p = 0.12, V = 0.07).

Basic summary

Front Psychol. 2026 Jul 17;17:1794715. doi: 10.3389/fpsyg.2026.1794715. eCollection 2026.

ABSTRACT

INTRODUCTION: In the contemporary landscape of information technology, the advent of artificial intelligence (AI) in music composition marks a transformative era. This study examines differences in emotional expression accuracy between AI-generated music and compositions by traditional musicians, with a particular focus on Suno AI within the theoretical framework of Plutchik’s “Wheel of Emotions.” Emotional expression accuracy is defined as the degree of correspondence between the intended target emotion of a musical piece and the emotion perceived by listeners.

METHODS: An empirical approach was adopted to compare AI-generated and human-composed music and to evaluate the accuracy of emotional expression across four primary high-intensity emotions (ecstasy, rage, grief, and terror). A total of 32 musical pieces were analyzed, comprising 16 compositions by traditional composers and 16 works generated by Suno AI. Survey data were collected from 300 university students without formal music training, yielding 283 valid responses.

RESULTS: The results revealed that emotional expression accuracy differed significantly between human-composed and AI-generated music for rage (χ² = 40.66, p < 0.001, Cramer’s V = 0.27), ecstasy (χ² = 25.53, p < 0.001, V = 0.21), and terror (χ² = 16.46, p < 0.001, V = 0.17), whereas no significant difference was observed for grief (χ² = 2.44, p = 0.12, V = 0.07). Across emotions, the largest accuracy gap was observed for rage (25.40%), followed by ecstasy (21.50%) and terror (17.00%), while the difference for grief was comparatively smaller (4.20%) and not statistically significant.

DISCUSSION: These findings suggest that the advantage of human-composed music over AI-generated music may vary across emotional categories, with more pronounced differences observed for high-arousal emotions. The results further indicate that, despite substantial advances in generative systems such as Suno AI, current AI models may still face limitations in representing high-intensity emotional states. Future research could further explore improvements in emotional conditioning mechanisms and training data diversity to enhance the emotional expression accuracy of AI-generated music.

PMID:42539384 | PMC:PMC13423637 | DOI:10.3389/fpsyg.2026.1794715

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