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arXiv AI By Fotini Kiriakopoulou, Vassilis Lyberatos, Spyridon Kantarelis, Athanasios Voulodimos, Giorgos Stamou

Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features

Read the original on arXiv AI →

The paper introduces an interpretable framework for singing emotion recognition that uses a compact set of singing-specific descriptors covering prosody, spectral features, phonetics, and vocal technique. By applying SHAP and GRAD-CAM, the authors link model predictions to measurable acoustic phenomena, showing that vocal technique descriptors reduce cross-singer variability and provide interpretable insights. Out‑of‑domain tests on VocalSet reveal that vibrato and pharyngeal techniques are key emotional cues, with vibrato’s importance depending on articulation rate.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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