Evaluating song aesthetics is challenging due to the multidimensional nature of musical perception and the scarcity of labeled data. We propose HEAR, a robust music aesthetic evaluation framework that combines: (1) a multi-source multi-scale representations module to obtain complementary segment- and track-level features, (2) a hierarchical augmentation strategy to mitigate overfitting, and (3) a hybrid training objective that integrates regression and ranking losses for accurate scoring and reliable top-tier song identification. Experiments demonstrate that HEAR consistently outperforms the baseline across all metrics on both tracks of the ICASSP 2026 SongEval benchmark. The code and trained model weights are available at https://github.com/Eps-Acoustic-Revolution-Lab/EAR_HEAR.

翻译：由于音乐感知的多维特性及标注数据的稀缺性，歌曲美学评估具有挑战性。本文提出HEAR，一个鲁棒的音乐美学评估框架，其结合了：（1）多源多尺度表征模块，以获取互补的片段级和曲目级特征；（2）层次化增强策略以缓解过拟合；（3）混合训练目标，该目标整合了回归损失和排序损失，以实现精确评分和可靠的高阶歌曲识别。实验表明，在ICASSP 2026 SongEval基准测试的两个赛道上，HEAR在所有指标上均持续优于基线方法。代码及训练好的模型权重可在 https://github.com/Eps-Acoustic-Revolution-Lab/EAR_HEAR 获取。