MuQ-Eval：一个用于AI音乐生成评估的开源单样本质量度量标准
MuQ-Eval: An Open-Source Per-Sample Quality Metric for AI Music Generation Evaluation
AI总结：
本文提出MuQ-Eval，通过训练轻量级预测头，在冻结的MuQ-310M特征上构建了一个开源单样本质量度量标准，实现了高的人类相关性，并在单个消费级GPU上实时运行。
摘要：
分布度度量如Fréchet音频距离无法对单个音乐片段评分且与人类判断相关性差，而唯一能实现高人类相关性的单样本学习度量标准是闭源的。我们介绍了MuQ-EVAL，这是一个由MusicEval数据集构建的开源单样本质量度量标准，该数据集包含31个文本到音乐系统生成的片段，并有专家质量评分。我们的最简单模型，在冻结特征上使用注意力池化和两层MLP，实现了系统级SRCC=0.957和句子级SRCC=0.838的人类平均意见分数。系统消融分析显示，没有额外的改进能显著提升冻结基线，表明冻结的MuQ表示已经捕捉到了质量相关的信息。编码器选择是主导的设计因素，超过了所有架构和训练决策。经过LoRA适应的模型，即使在150个片段上训练，也实现了可接受的相关性，使个性化质量评估器从单个听众注释中实现。受控降级分析揭示了对信号级伪影的有选择性敏感，但对音乐结构扭曲的不敏感。我们的度量标准，MuQ-EVAL，是完全开源的，优于现有的开源单样本度量标准，并能在单个消费级GPU上实时运行。代码、模型权重和评估脚本可在https://github.com/dgtql/MuQ-Eval上获得。
英文摘要：
Distributional metrics such as Fréchet Audio Distance cannot score individual music clips and correlate poorly with human judgments, while the only per-sample learned metric achieving high human correlation is closed-source. We introduce MUQ-EVAL, an open-source per-sample quality metric for AIgenerated music built by training lightweight prediction heads on frozen MuQ-310M features using MusicEval, a dataset of generated clips from 31 text-to-music systems with expert quality ratings. Our simplest model, frozen features with attention pooling and a two-layer MLP, achieves system-level SRCC = 0.957 and utterance-level SRCC = 0.838 with human mean opinion scores. A systematic ablation over training objectives and adaptation strategies shows that no addition meaningfully improves the frozen baseline, indicating that frozen MuQ representations already capture quality-relevant information. Encoder choice is the dominant design factor, outweighing all architectural and training decisions. LoRA-adapted models trained on as few as 150 clips already achieve usable correlation, enabling personalized quality evaluators from individual listener annotations. A controlled degradation analysis reveals selective sensitivity to signal-level artifacts but insensitivity to musical-structural distortions. Our metric, MUQ-EVAL, is fully open-source, outperforms existing open per-sample metrics, and runs in real time on a single consumer GPU. Code, model weights, and evaluation scripts are available at https://github.com/dgtql/MuQ-Eval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
