MMMOS: Multi-domain Multi-axis Audio Quality Assessment

TL;DR

MMMOS is a novel multi-axis audio quality assessment system that accurately estimates diverse perceptual factors across various audio domains, outperforming existing models in generalization and precision.

Contribution

It introduces a multi-domain, multi-axis no-reference audio quality assessment framework using ensemble learning and multiple pretrained encoders, addressing limitations of single-score models.

Findings

20-30% reduction in mean squared error

4-5% increase in Kendall's tau

First place in six of eight Production Complexity metrics

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.