Perception of AI-Generated Music -- The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness
David Stammer, Hannah Strauss, Peter Knees
TL;DR
This study investigates how composer identity, listener attitudes toward AI, personality traits, and perceived humanness shape the perception of AI-generated music. Employing a mixed-method design, stimuli from two AI music tools across multiple genres were rated under varying origin-label conditions, with comprehensive measures of emotion, personality, and attitudes. Key findings show attitudes toward AI are the strongest predictors of liking and emotional intensity, while perceived humanness also enhances both metrics; composer information effects largely vanish when AI attitudes are accounted for. The results highlight the contextual and normative nature of evaluating AI-generated music and underscore the importance of transparency and audience dispositions in human-AI musical perception research.
Abstract
The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and listener characteristics shape the perception of AI-generated music, adopting a mixed-method approach. Using a diverse set of stimuli across various genres from two AI music models, we examine effects of perceived authorship on liking and emotional responses, and explore how attitudes toward AI, personality traits, and music-related variables influence evaluations. We further assess the influence of perceived humanness and analyze open-ended responses to uncover listener criteria for judging AI-generated music. Attitudes toward AI proved to be the best predictor of both liking and emotional intensity of AI-generated music. This quantitative finding was complemented by qualitative themes from our thematic analysis, which identified ethical, cultural, and contextual considerations as important criteria in listeners' evaluations of AI-generated music. Our results offer a nuanced view of how people experience music created by AI tools and point to key factors and methodological considerations for future research on music perception in human-AI interaction.