Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift
Summary
PRISM is a training-free test-time adaptation method that reverses low-rank affine noise distortions in audio-text models using frozen text prototypes, showing significant improvements under severe acoustic noise.
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Paper page - Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift
Source: https://huggingface.co/papers/2608.15037
Abstract
PRISM is a fast, training-free test-time adaptation method that reverses low-rank affine noise distortions in audio-text models using frozen text prototypes and geometric corrections.
Audio-Text Foundation Models(ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-basedTest-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 theAffine 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 reverses this distortion from an unlabeled target batch using frozen text prototypes as geometric anchors via three closed-form geometric corrections compiled into a single static projection matrix byAffine Bias Regression. At inference, adaptation reduces to one matrix-vector multiplication in 0.0009 ms, making it substantially faster than gradient-based TTA while requiring no additional training. On UrbanSound8K, PRISM improves over the zero-shot baseline by 12.94 percentage points and surpasses an oracle-assisted TTA baseline by 9.41 percentage points, despite never observing its privileged augmented noise prompts. We further identify thePolyphonic Trap, a principled failure mode of subspace deflation for broadband classes, and resolve it viaConfidence-Aware Regression(CAR), recovering up to 8.16 percentage points for the worst-affected class.
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