Echoes of Humanity: Exploring the Perceived Humanness of AI Music
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908568083496960 |
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| author | Figueiredo, Flavio Martinelli, Giovanni Sousa, Henrique Rodrigues, Pedro Pedrosa, Frederico Ferreira, Lucas N. |
| author_facet | Figueiredo, Flavio Martinelli, Giovanni Sousa, Henrique Rodrigues, Pedro Pedrosa, Frederico Ferreira, Lucas N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Echoes of Humanity: Exploring the Perceived Humanness of AI Music Figueiredo, Flavio Martinelli, Giovanni Sousa, Henrique Rodrigues, Pedro Pedrosa, Frederico Ferreira, Lucas N. Artificial Intelligence Human-Computer Interaction Sound 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. |
| title | Echoes of Humanity: Exploring the Perceived Humanness of AI Music |
| topic | Artificial Intelligence Human-Computer Interaction Sound |
| url | https://arxiv.org/abs/2509.25601 |