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DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation

arXiv 2511.18421 ↗First posted 23 Nov 2025cs.SD cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 23 Nov 2025.

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26 of 50 references.

References · 26

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  4. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,850
  5. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
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  9. 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 574
  10. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
  11. 2022Masked Autoencoders that Listencited by 273
  12. 2021SSAST: Self-Supervised Audio Spectrogram Transformercited by 169
14 more
  1. 2021The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalizationcited by 131
  2. 2018LPCNet: Improving Neural Speech Synthesis Through Linear Predictioncited by 110
  3. 2018AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmarkcited by 72
  4. 2022Vocalsound: A Dataset for Improving Human Vocal Sounds Recognitioncited by 62
  5. 2019A scalable noisy speech dataset and online subjective test frameworkcited by 48
  6. 2022CochlScene: Acquisition of acoustic scene data using crowdsourcingcited by 33
  7. 2022CoNMix for Source-free Single and Multi-target Domain Adaptationcited by 23
  8. 2022Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognitioncited by 19
  9. 2024Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speechcited by 12
  10. 2020Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Networkcited by 8
  11. 2020Mel-spectrogram augmentation for sequence to sequence voice conversioncited by 7
  12. 2021Neural Pitch-Shifting and Time-Stretching with Controllable LPCNetcited by 4
  13. 2025An Investigation of Test-time Adaptation for Audio Classification under Background Noisecited by 1
  14. 2025AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratchcited by 1

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