用声学与声乐技巧特征做可解释的歌唱情感识别
Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features
这篇把歌唱当独立信号来分析，用 SHAP 指出颤音、咽音才是情感线索，做音频或音乐AI的可以看看思路。
论文提出一套可解释的歌唱情感识别框架，使用覆盖韵律、频谱、语音学和声乐技巧的紧凑特征集，而非纯黑盒深度模型。作者用 SHAP 解码模型决策，并用 GRAD-CAM 定性评估声乐技巧的表征学习。在 VocalSet 数据集上的域外评估显示，颤音和咽音技巧是情感表达的关键线索，SHAP 还表明颤音的作用依赖于咬字速率。歌唱专用特征能降低跨歌手的个体差异。
Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features
Singing emotion recognition relies predominantly on deep learning models that achieve strong performance but offer limited insight into the vocal characteristics driving emotion, of- ten overlooking the singing voice as a distinct signal. We propose an interpretable singing emotion recognition framework using a compact set of singing-specific descriptors across prosody, spec- tral features, phonetics, and vocal technique. To decode model decisions and link predictions to measurable acoustic phenomena, we employ SHAP and apply GRAD-CAM to qualitatively assess the learned representations of vocal techniques. Evaluations show that singing-specific features reduce cross-singer variability, while vocal technique descriptors provide interpretable insights into singing expression. Furthermore, out-of-domain evaluation on VocalSet reveals interesting associations between emotional expression and ground-truth technical execution. The analysis highlights vibrato and pharyngeal techniques as salient cues of emotional delivery, with SHAP indicating that vibrato is context-dependent on articulation rate. These findings emphasize the value of domain-specific features for interpretable singing emotion analysis.