◇ Paper · Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift

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    "summary": "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 Mani",
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      "title": "Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift",
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        "Rini Smita Thakur",
        "Aryan Das",
        "Vinod K. Kurmi"
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        "summary": "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 tha",
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