提出新方法提升文本生成音乐的自动评估效果
DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
- 分步优化音乐印象与文本对齐，用列表排序损失增强相关性
- 在MusicEval上使两项评价指标显著提升，排名相关性更强
- 适合需要高效、准确评估生成音乐质量的研究者
文本到音乐（TTM）系统评估成本高昂，因音乐印象（MI）和文本对齐（TA）分数依赖人工均值评分（MOS）。现有自动MOS估计算法多采用点对点回归或分布分类训练，未直接优化基于排名的指标，且对跨模态一致性约束较弱。为此，本文提出DeRA-MOS，一种解耦的TTM评估优化框架：针对MI，引入批处理感知的列表排序损失，建模每个小批次内的相对顺序，更契合斯皮尔曼等级相关系数（SRCC）评估标准；针对TA，提出基于得分锚定的模态对齐损失，将人类评分映射至目标音文相似度，并在融合前正则化潜在空间。实验证明，该解耦框架有效缓解了点对点训练偏差与模态漂移问题，在MusicEval数据集上显著提升MI与TA的排名性能，建立了一套稳健的大规模TTM评估范式。
原文摘要 · Abstract (English)
Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS). Most automatic MOS estimators are trained with point-wise regression or distributional classification. These objectives do not directly optimize rank-based metrics and provide weak geometric constraints for cross-modal coherence. To address these gaps, we propose DeRA-MOS, a decoupled optimization framework for TTM evaluation. For MI, we introduce a batch-aware listwise ranking loss that models relative order within each mini-batch and better aligns with evaluation based on Spearman's rank correlation coefficient (SRCC). For TA, we introduce a score-anchored modality alignment loss that maps human scores to target audio-text similarity and regularizes the latent space before fusion. By effectively mitigating the point-wise training mismatch and modality drift, experiments on MusicEval demonstrate that our decoupled framework yields substantial improvements in both MI and TA ranking metrics, establishing a robust paradigm for large-scale TTM evaluation.
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