评测AI生成歌曲的审美评分，推动音乐生成更符合人类喜好。
The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge
- 分两赛道预测整体与五维细粒度审美分数
- 顶尖模型显著超越基线，贴近人类评价
- 为音乐生成提供可比的审美评估标准
本文总结了ICASSP 2026年自动歌曲审美评价（ASAE）挑战赛，聚焦于预测AI生成歌曲的主观审美得分。挑战包含两个赛道：赛道1针对整体音乐性评分预测，赛道2则关注五个细粒度审美维度的评分。该挑战吸引了学术界与产业界的广泛关注，收到大量投稿。表现最优的系统显著超越官方基线，表明客观指标与人类审美偏好之间的对齐已取得实质性进展。成果确立了标准化评估基准，推动了现代音乐生成系统的人类对齐评价方法发展。
原文摘要 · Abstract (English)
This paper summarizes the ICASSP 2026 Automatic Song Aesthetics Evaluation (ASAE) Challenge, which focuses on predicting the subjective aesthetic scores of AI-generated songs. The challenge consists of two tracks: Track 1 targets the prediction of the overall musicality score, while Track 2 focuses on predicting five fine-grained aesthetic scores. The challenge attracted strong interest from the research community and received numerous submissions from both academia and industry. Top-performing systems significantly surpassed the official baseline, demonstrating substantial progress in aligning objective metrics with human aesthetic preferences. The outcomes establish a standardized benchmark and advance human-aligned evaluation methodologies for modern music generation systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。