Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift
Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-based Test-Time Adaptation (TTA), which reinforces noise rather than signal, or on prompt tuning that requires privileged noise annotations unavailable at inference. We address these failures with PRISM (Prototype-Rectified Iterative Self-supervised Manifold Denoising), a training-free, source-free TTA framework grounded in the Affine Noise Hypothesis: severe acoustic noise induces a low-rank affine shift in the multimodal latent space, with more than 90% of distortion energy confined to the leading 60 principal components. PRISM estimates and r...
| Authors | Ashish Anand Shukla, Rini Smita Thakur, Aryan Das et al. |
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| Primary category | cs.SD |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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