AI 中文总结
本文提出PADP方法，通过引入感知无关的音频扰动来测试音频质量模型的感知能力，发现现有模型与人类听觉感知存在显著偏差。
AI 中文摘要
本文提出了一组引入感知无关失真的音频变换，并展示了它们作为音频质量模型感知压力测试的用途。我们将这些方法称为感知音频数据扰动（PADP）。PADP利用人类听觉系统对某些细微信号变化的不敏感性，在保持感知音频质量和内容的同时大幅改变波形。通过受控听力测试评估PADP的可听性及其参数选择，确保变换对非关键和关键项目均达到透明或接近透明的质量。我们进一步探测了最先进的（SOTA）感知动机客观音频质量模型和基础模型的鲁棒性。结果揭示了模型响应与人类听觉感知之间的错位，突显了这些模型对某些所提出变换的感知意识有限。
英文摘要
This paper presents a collection of audio transforms that introduce perceptually irrelevant distortions and demonstrates their use as perceptual stress tests for audio quality models. We refer to these methods as Perceptual Audio Data Perturbation (PADP). PADP exploits the insensitivity of the human auditory system to certain fine-grained signal variations to substantially alter the waveform while preserving perceived audio quality and content. The audibility of PADP and the selection of its parameters are evaluated through controlled listening tests, ensuring that the transformations achieve transparent or near-transparent quality for both non-critical and critical items. We further probe the robustness of state-of-the-art (SOTA) perception-motivated objective audio quality models and foundation models. The results reveal a misalignment between model responses and human auditory perception, highlighting the limited perceptual awareness of these models for certain proposed transforms.
Comments5 pages, 3 figures
