MMMOS: Multi-domain Multi-axis Audio Quality Assessment
This addresses the need for more nuanced audio quality evaluation across diverse domains, though it is incremental as it builds on existing encoder architectures.
The paper tackles the problem of limited generalization in audio quality assessment by developing MMMOS, a multi-domain system that estimates four perceptual axes across speech, music, and environmental sounds, achieving a 20-30% reduction in mean squared error and ranking top-three on 17 of 32 challenge metrics.
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.