多领域音频质量评估新模型，可细分四维度评分
MMMOS: Multi-domain Multi-axis Audio Quality Assessment
- 融合三个预训练编码器的帧级特征，分维度评估音频质量
- 在32项指标中17项进入前三，复杂度评估排名第一
- 适合语音、音乐和环境音的多场景质量分析
精准的音频质量评估对音频生成、检索与增强系统的发展与评价至关重要。现有非侵入式评估模型仅针对语音输出单一平均意见分（MOS），合并了多种感知因素且泛化能力差。我们提出MMMOS，一种无参考的多领域音频质量评估系统，可在语音、音乐和环境音中估计四个正交维度：制作质量、制作复杂度、内容愉悦度和内容实用性。MMMOS融合来自三个预训练编码器（WavLM、MuQ、M2D）的帧级嵌入，并评估三种聚合策略与四种损失函数。通过集成表现最优的八组模型，MMMOS在均方误差上降低20-30%，肯德尔τ相关系数提升4-5%；在八项指标中的六项生产复杂度评估中位列第一，在32项挑战指标中17项排名前三。
原文摘要 · Abstract (English)
Accurate audio quality estimation is essential for developing and evaluating audio generation, retrieval, and enhancement systems. Existing non-intrusive assessment models predict a single Mean Opinion Score (MOS) for speech, merging diverse perceptual factors and failing to generalize beyond speech. We propose MMMOS, a no-reference, multi-domain audio quality assessment system that estimates four orthogonal axes: Production Quality, Production Complexity, Content Enjoyment, and Content Usefulness across speech, music, and environmental sounds. MMMOS fuses frame-level embeddings from three pretrained encoders (WavLM, MuQ, and M2D) and evaluates three aggregation strategies with four loss functions. By ensembling the top eight models, MMMOS shows a 20-30% reduction in mean squared error and a 4-5% increase in Kendall's τ versus baseline, gains first place in six of eight Production Complexity metrics, and ranks among the top three on 17 of 32 challenge metrics.
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