MMMOS: Multi-domain Multi-axis Audio Quality Assessment

Yi-Cheng Lin; Jia-Hung Chen; Hung-yi Lee

doi:10.1109/asru65441.2025.11433844

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arXiv · 2507.04094

MMMOS: Multi-domain Multi-axis Audio Quality Assessment

Yi-Cheng Lin·Jia-Hung Chen·Hung-yi Lee

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Abstract

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 {\tau} 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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BibTeX RIS

Yi-Cheng Lin, Jia-Hung Chen, Hung-yi Lee. 2025-07-05. MMMOS: Multi-domain Multi-axis Audio Quality Assessment. https://doi.org/10.1109/asru65441.2025.11433844

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