构建动态异构音频测试时自适应评估基准，揭示传统方法隐藏的鲁棒性缺陷。
DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation
- 设计动态噪声强度与异构噪声混合的音频退化模拟机制
- 在真实场景噪声下暴露现有TTA方法的性能瓶颈
- 为语音分类模型提供更贴近现实的鲁棒性评估标准
现有测试时自适应（TTA）研究严重依赖静态、同质的噪声协议，如ImageNet-C和CIFAR-10-C/100-C，导致评估设置不一致，可能高估模型在真实场景中的鲁棒性。当前TTA缺乏能模拟真实异构声学退化的标准化评估基础设施。本文提出DHAuDS，一个标准化的基准套件，用于评估音频分类在动态噪声强度与异构噪声混合下的TTA鲁棒性。DHAuDS并非提出新算法，而是旨在揭示在传统固定噪声评估协议下被掩盖的鲁棒性局限。
原文摘要 · Abstract (English)
Existing Test-time Adaptation (TTA) studies rely heavily on static and homogeneous corruption protocols, such as ImageNet-C and CIFAR-10-C/100-C, leading to inconsistent evaluation settings and potentially inflated robustness estimates that are compared with real-world situations. TTA lacks a standardized evaluation infrastructure capable of modeling realistic heterogeneous acoustic degradation. We introduce DHAuDS, a standardized benchmark suite for evaluating audio classification TTA robustness under dynamic corruption severity and heterogeneous noise mixtures. Rather than proposing a new TTA algorithm, DHAuDS focuses on exposing robustness limitations that remain hidden under conventional fixed-noise evaluation protocols.
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