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Echoes of Humanity: Exploring the Perceived Humanness of AI Music

arXiv 2509.25601 ↗First posted 29 Sep 2025cs.AI cs.HC cs.SDcited by 0

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Abstract (arXiv)

Recent advances in AI music (AIM) generation services are currently transforming the music industry. Given these advances, understanding how humans perceive AIM is crucial both to educate users on identifying AIM songs, and, conversely, to improve current models. We present results from a listener-focused experiment aimed at understanding how humans perceive AIM. In a blind, Turing-like test, participants were asked to distinguish, from a pair, the AIM and human-made song. We contrast with other studies by utilizing a randomized controlled crossover trial that controls for pairwise similarity and allows for a causal interpretation. We are also the first study to employ a novel, author-uncontrolled dataset of AIM songs from real-world usage of commercial models (i.e., Suno). We establish that listeners' reliability in distinguishing AIM causally increases when pairs are similar. Lastly, we conduct a mixed-methods content analysis of listeners' free-form feedback, revealing a focus on vocal and technical cues in their judgments.

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References · 16

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  11. 2025Survey on the Evaluation of Generative Models in Musiccited by 10
  12. 2020TensorFlow Audio Models in Essentiacited by 9
4 more
  1. 2022Subjective Evaluation of Deep Learning Models for Symbolic Music Compositioncited by 5
  2. 2025When Incentives Backfire, Data Stops Being Humancited by 4
  3. 2024Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Musiccited by 3
  4. 2025Understanding Listener Perceptions of AI and Human-Composed Music in Emotional Applicationscited by 1

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