发表机构
Fraunhofer Institute for Integrated Circuits IIS; International Audio Laboratories Erlangen; Friedrich-Alexander Universität Erlangen-Nürnberg（弗劳恩霍夫集成电路研究所 IIS; 埃尔朗根国际音频实验室; 纽伦堡弗里德里希-亚历山大大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种轻量级侵入式音频质量评估方法，结合PEAQ前端与装袋集成回归器，通过校准生成分数分布，提供置信区间和不确定性评估，性能媲美数据密集型端到端方法。
AI 中文摘要
客观音频质量指标通常提供点估计，而听力测试则产生分数分布，从中得出平均意见分数（MOS）、置信区间（CI）和显著性决策。我们提出了一种轻量级侵入式指标，将PEAQ风格的感知前端（ITU-R BS.1387）与回归器的装袋集成相结合。用主观数据进行校准，使集成输出与听众分数对齐。由此产生的依赖于项目的分数分布能够进行不确定性评估、匹配小组规模的置信区间，以及识别不太确定的预测。校准提高了与主观分布的一致性以及所有评估数据集上的CI覆盖率，同时保持了MOS准确性。仅使用11个固定的PEAQ特征、低容量回归器和公共训练数据，该方法的表现与更依赖数据的端到端方法相当。其输出可以支持不确定性感知评估，并将目标听力测试引导至不确定条件。这些分布还支持近似的成对比较，但尚不能进行可靠的显著性推断。
英文摘要
Objective audio quality metrics typically provide point estimates, whereas listening tests yield score distributions from which mean opinion scores (MOS), confidence intervals (CIs), and significance decisions are derived. We propose a lightweight intrusive metric that combines a PEAQ-style perceptual front-end (ITU-R BS.1387) with a bagging ensemble of regressors. Calibration with subjective data aligns ensemble outputs with listener scores. The resulting item-dependent score distributions enable uncertainty assessment, panel-size-matched CIs, and identification of less conclusive predictions. Calibration improves agreement with subjective distributions and CI coverage across all evaluated datasets while preserving MOS accuracy. Using only 11 fixed PEAQ features, low-capacity regressors, and public training data, the method performs comparably to more data-intensive end-to-end approaches. Its output can support uncertainty-aware assessment and target listening tests towards uncertain conditions. The distributions also enable approximate pairwise comparisons, but not yet reliable significance inference.
Comments5 Pages. 3 Figures. Submitted to 2027 IEEE International Conference on Acoustics, Speech, and Signal Processing
