arXiv cs.AI· Fotini Kiriakopoulou, Vassilis Lyberatos, Spyridon Kantarelis, Athanasios Voulodimos, Giorgos Stamou·· 3 小时前AI 评分20
基于声学与声乐技巧特征的可解释演唱情感识别
Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features
AI 导读
研究者提出一种可解释的演唱情感识别框架，使用涵盖韵律、频谱、语音和声乐技巧的紧凑演唱专用特征集，结合 SHAP 和 GRAD-CAM 解码模型决策。评估显示演唱专用特征能降低跨歌手差异，在 VocalSet 的域外评测中发现颤音和咽音技巧是情感表达的关键线索，且颤音的作用依赖于咬字速率。
正文 · 原文
Abstract: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.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32629 [cs.SD] |
| (or arXiv:2610.32629v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32629 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fotini Kiriakopoulou [view email]
[v1]
Fri, 23 Oct 2026 13:42:34 UTC (1,074 KB)
来源：arXiv cs.AI · arxiv.org