人类如何识别AI生成音乐？实验发现相似度越高越难分辨。
Echoes of Humanity: Exploring the Perceived Humanness of AI Music
- 通过盲测实验对比AI与真人创作音乐的辨识度
- 当歌曲相似时，人类辨别AI音乐的准确率显著上升
- 听众主要依赖人声和音技术细节判断来源，适合音乐感知研究者参考
近年来，AI音乐生成服务正深刻改变音乐产业。为理解人类对AI生成音乐的感知，我们开展一项以听者为中心的实验。在盲测、类图灵测试环境下，参与者需从一对歌曲中辨别出AI生成作品。本研究采用随机对照交叉实验设计，控制配对歌曲的相似性，实现因果推断。首次使用真实商业模型（Suno）在实际使用中产生的未受控AI音乐数据集。结果表明，当歌曲对相似时，听者辨别AI音乐的能力显著提升。此外，结合自由反馈的混合方法内容分析显示，听者主要依据人声特征和技术细节做出判断。
原文摘要 · Abstract (English)
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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