# Perception of AI-Generated Music -- The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness

arXiv: https://arxiv.org/abs/2512.02785 · HTML: https://mlsanity.com/paper/2512.02785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [Mousai: Text-to-Music Generation with Long-Context Latent Diffusion](https://mlsanity.com/paper/2301.11757/) (2023)
- [A Functional Taxonomy of Music Generation Systems](https://mlsanity.com/paper/1812.04186/) (2018)
- [From Artificial Neural Networks to Deep Learning for Music Generation -- History, Concepts and Trends](https://mlsanity.com/paper/2004.03586/) (2020)
- [Deep Performer: Score-to-Audio Music Performance Synthesis](https://mlsanity.com/paper/2202.06034/) (2022)

## Abstract (arXiv)

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.