Representation Matters in Randomized Smoothing for Audio Classification
Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uniquely defined as standard pipelines normalize, range-control, and transform waveforms into log-mel or other spectral features. We show that direct RS is therefore under-specified unless the certified object and preprocessing policy are explicit. On two audio benchmarks, keyword spotting and environmental-sound classification, we study waveform, feature-space, and post-processed smoothing. Our diagnostics show why representation-aware reporting is necessary: at the same smoothing level $σ=0.0025$, the two datasets share the same median raw ra...
| Authors | Jong-Ik Park, Shreyas Chaudhari, José M. F. Moura et al. |
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| Primary category | eess.AS |
| Published | 2 June 2026 |
| Last updated at source | 28 June 2026 |
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