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

• URL: http://arxiv.org/abs/2511.18421v1
• Date: Sun, 23 Nov 2025 12:19:23 GMT
• Title: DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation
• Abstract summary: Test-Time Adaptation (TTA) research in speech and sound analysis often evaluates models under fixed or mismatched noise settings.<n>This paper presents DHAuDS, a benchmark designed to assess TTA approaches under more realistic and diverse acoustic shifts.<n>DHAuDS comprises four standardized benchmarks: UrbanSound8K-C, SpeechCommandsV2-C, VocalSound-C, and ReefSet-C.
• Score: 0.3728263002609659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio classifiers frequently face domain shift, when models trained on one dataset lose accuracy on data recorded in acoustically different conditions. Previous Test-Time Adaptation (TTA) research in speech and sound analysis often evaluates models under fixed or mismatched noise settings, that fail to mimic real-world variability. To overcome these limitations, this paper presents DHAuDS (Dynamic and Heterogeneous Audio Domain Shift), a benchmark designed to assess TTA approaches under more realistic and diverse acoustic shifts. DHAuDS comprises four standardized benchmarks: UrbanSound8K-C, SpeechCommandsV2-C, VocalSound-C, and ReefSet-C, each constructed with dynamic corruption severity levels and heterogeneous noise types to simulate authentic audio degradation scenarios. The framework defines 14 evaluation criteria for each benchmark (8 for UrbanSound8K-C), resulting in 50 unrepeated criteria (124 experiments) that collectively enable fair, reproducible, and cross-domain comparison of TTA algorithms. Through the inclusion of dynamic and mixed-domain noise settings, DHAuDS offers a consistent and publicly reproducible testbed to support ongoing studies in robust and adaptive audio modeling.

Related papers

• Audio-Visual Turn-taking Prediction in Cocktail Party Scenarios [10.275726926689861]
  Current predictive turn-taking models (PTTMs) achieve strong performance on benchmarks with controlled acoustic conditions and clean audio signals.<n>We evaluate audio-visual PTTMs trained with clean data on a challenging cocktail-party testbed derived from the AVCocktail dataset.
  arXiv  Detail & Related papers  (2026-09-15T12:06:16Z)
• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions [54.34001921326444]
  Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems.<n>Existing benchmarks evaluate only subsets of these conditions, missing others entirely.<n>We introduce SVeritas, a comprehensive Speaker Verification tasks benchmark suite, assessing SV systems under stressors like recording duration, spontaneity, content, noise, microphone distance, reverberation, channel mismatches, audio bandwidth, codecs, speaker age, and susceptibility to spoofing and adversarial attacks.
  arXiv  Detail & Related papers  (2025-09-21T14:11:16Z)
• An Investigation of Test-time Adaptation for Audio Classification under Background Noise [0.0]
  This research aims to address the issue of audio classification under domain shift caused by background noise using Test-Time Adaptation (TTA)<n>We adopt two common TTA methods, TTT and TENT, and a state-of-the-art method CoNMix, and investigate their respective performance on two popular audio classification datasets.<n>The experimental results reveal that our proposed modified version of CoNMix produced the highest classification accuracy under domain shift.
  arXiv  Detail & Related papers  (2025-07-21T11:44:24Z)
• Effective Noise-aware Data Simulation for Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation [25.410770364140856]
  Cross-domain speech enhancement (SE) is often faced with severe challenges due to the scarcity of noise and background information in an unseen target domain. This study puts forward a novel data simulation method to address this issue, leveraging noise-extractive techniques and generative adversarial networks (GANs) We introduce the notion of dynamic perturbation, which can inject controlled perturbations into the noise embeddings during inference.
  arXiv  Detail & Related papers  (2024-09-03T02:29:01Z)
• D4AM: A General Denoising Framework for Downstream Acoustic Models [45.04967351760919]
  Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. Existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. We propose a general denoising framework, D4AM, for various downstream acoustic models.
  arXiv  Detail & Related papers  (2023-11-28T08:27:27Z)
• Advancing Test-Time Adaptation in Wild Acoustic Test Settings [26.05732574338255]
  Speech signals follow short-term consistency, requiring specialized adaptation strategies. We propose a novel wild acoustic TTA method tailored for ASR fine-tuned acoustic foundation models. Our approach outperforms existing baselines under various wild acoustic test settings.
  arXiv  Detail & Related papers  (2023-10-14T06:22:08Z)
• DiffSED: Sound Event Detection with Denoising Diffusion [70.18051526555512]
  We reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions.
  arXiv  Detail & Related papers  (2023-08-14T17:29:41Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training [16.807298318504156]
  We show that we can improve performance by dynamically routing samples to specialized feature encoders for each noise condition. We extend these improvements to the multimodal setting by dynamically routing samples to maintain temporal ordering.
  arXiv  Detail & Related papers  (2020-10-21T18:07:32Z)
• Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification [61.54074498090374]
  This study introduces a CRSS-Forensics audio dataset collected in multiple acoustic environments. We pre-train a CNN-based network using the VoxCeleb data, followed by an approach which fine-tunes part of the high-level network layers with clean speech from CRSS-Forensics.
  arXiv  Detail & Related papers  (2020-09-05T02:54:33Z)
• Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching [69.24460241328521]
  Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions. We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
  arXiv  Detail & Related papers  (2020-04-30T23:56:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.