Drift-Augmented Scoring: Text-Derived Noise Robustness for Zero-Shot Audio-Language Classification
Contrastive audio-language models such as CLAP enable zero-shot audio classification: a sound is labelled by matching its embedding to text prompt embeddings, with no labelled audio. This matching breaks down under acoustic noise, where accuracy and mAP fall by 12-30 percentage points at 0 dB SNR on standard benchmarks. We propose Drift Augmented Scoring (DAS), a small per-class bonus added to the cosine score. The bonus rewards a class when the noisy audio embedding drifts in the direction that the class's noise-conditioned text prompts predict. It is derived from text alone, computed once and cached, and adds a single inner product per class at inference, with no gradients and no test-t...
| Authors | Tu Vo, Sheir Zaheer, Chan Y. Park |
|---|---|
| Primary category | cs.SD |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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