Understanding Listener Perceptions of AI and Human-Composed Music in Emotional Applications

Abstract

Designing music-based affective technologies requires understanding of how perceptions of AI versus human authorship shape trust and authenticity. We investigate how listener perception of AI-generated versus human-composed music affects emotional resonance and regulation. Drawing on affective computing and human-computer interaction frameworks, participants listened to AI- and human-composed music across labeling conditions (Correct, Incorrect, or Unlabeled) and emotion cases (Calm and Upbeat). Participants rated preference, efficacy of target emotion elicitation, and emotional impact. Results showed participants found human-composed music more effective in eliciting their target affective states and linked humanness to imperfection, flow, and "soul," underscoring authenticity as central to appraisal and ultimately leading to design implications relevant to music-based HCI. These findings challenge the assumption that preference alone defines system success, highlighting design implications for affective and wellness technologies that foreground authenticity, transparency, and human creativity.