RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition

Abstract:Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in everyday environments. High-resolution (HR) audio provides rich acoustic information for model development but incurs substantial energy and storage costs and may expose sensitive speech content. Low-resolution (LR) audio offers a more privacy-preserving and resource-efficient alternative for deployment, but reduced sampling rates can remove acoustic cues essential for activity recognition, leading to significant performance degradation. We formulate this training-deployment mismatch as sensor-resolution privileged learning, in which HR audio is available during training, while inference relies exclusively on LR audio. We propose RAST, a resolution-aware transfer framework that compresses HR teacher representations by preserving token-level information and neighborhood structure before performing localized HR-LR alignment. Experiments on the SAMoSA and AudioIMU datasets show that RAST consistently outperforms LR-only training and direct teacher-transfer baselines, improving LR-only recognition by up to approximately 7.8% while requiring only LR audio at inference.

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

arXiv:2609.38780 [cs.SD]

(or arXiv:2609.38780v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.38780 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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