Multichannel Audio Quality Assessment: Extending Pretrained Perceptual Models to Spatial Audio

Abstract:Accurate perceptual quality assessment is essential for evaluating and optimizing spatial audio, where perceived quality depends on both signal fidelity and inter-channel spatial relationships. However, subjective evaluation is costly, while existing perceptual models are often trained for limited channel configurations and cannot be directly applied to higher-channel-count audio. This raises the question: how can pretrained perceptual knowledge be effectively reused for multichannel spatial audio? Using 5.1-channel audio, we study four levels of multichannel integration: signal, prediction, latent, and feature and propose two learned approaches: latent-level aggregation of spatial-group representations and the feature-level Feature-Band Group Attention (FGAtt), which adaptively fuses spatial groups at the feature level before perceptual processing. Across five 5.1-channel test sets, FGAtt achieves the strongest over- all performance, demonstrating the effectiveness of feature-level adaptation for reusing pretrained perceptual knowledge

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2609.37116 [cs.SD]

(or arXiv:2609.37116v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.37116 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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