We propose an audio aesthetics score (AES) prediction system by CyberAgent (AESCA) for AudioMOS Challenge 2025 (AMC25) Track 2. The AESCA comprises a Kolmogorov--Arnold Network (KAN)-based audiobox aesthetics and a predictor from the metric scores using the VERSA toolkit. In the KAN-based predictor, we replaced each multi-layer perceptron layer in the baseline model with a group-rational KAN and trained the model with labeled and pseudo-labeled audio samples. The VERSA-based predictor was designed as a regression model using extreme gradient boosting, incorporating outputs from existing metrics. Both the KAN- and VERSA-based models predicted the AES, including the four evaluation axes. The final AES values were calculated using an ensemble model that combined four KAN-based models and a VERSA-based model. Our proposed T12 system yielded the best correlations among the submitted systems, in three axes at the utterance level, two axes at the system level, and the overall average. We also released the inference model of the proposed KAN-based predictor (KAN #1-#4).

翻译：我们为AudioMOS Challenge 2025（AMC25）赛道2提出了一种音频美学评分预测系统（AESCA）。该系统由基于Kolmogorov-Arnold Network的音频美学评估模块与基于VERSA工具包的指标分数预测器构成。在基于KAN的预测器中，我们将基准模型中的每个多层感知机层替换为分组有理KAN层，并使用标注及伪标注音频样本进行训练。基于VERSA的预测器设计为采用极端梯度提升的回归模型，整合了现有度量指标的输出。KAN与VERSA模型均可预测包含四个评估维度的音频美学评分。最终评分通过集成四个KAN模型与一个VERSA模型的集成模型计算得出。我们提出的T12系统在提交系统中取得了最佳相关性：在语句级别三个维度、系统级别两个维度及整体平均分上均表现最优。同时我们开源了所提出的KAN预测器（KAN #1-#4）的推理模型。