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.

翻译：AI生成艺术的迅速兴起引发了关于受众对此类作品感知与评价潜在偏见的讨论。本研究采用混合方法，探讨作曲家信息与听者特征如何塑造对AI生成音乐的感知。通过使用来自两个AI音乐模型、涵盖多种流派的多样化刺激材料，我们检验了感知作者身份对喜好与情绪反应的影响，并探究了对AI的态度、人格特质及音乐相关变量如何影响评价。我们进一步评估了感知人性化的影响，并分析了开放式回答以揭示听者评判AI生成音乐的标准。研究发现，对AI的态度是预测AI生成音乐的喜好与情绪强度的最佳指标。这一量化结果得到了主题分析中定性主题的补充，该分析将伦理、文化及情境考量确定为听者评价AI生成音乐的重要标准。我们的结果为人们如何体验AI工具创作的音乐提供了细致入微的视角，并为未来人机交互中音乐感知研究指出了关键因素与方法学考量。