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.
arXiv:2608.28932v1 Announce Type: new Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-o...
The paper introduces a unified multitask learning framework for Music Emotion Recognition that simultaneously handles categorical and dimensional emotion labels, enabling training across multiple datasets. It leverages musical features such as key and chords, MERT embeddings, and employs knowledge distillation from teacher models to a student model to improve generalization. Experiments on MTG‑Jamendo, DEAM, PMEmo, and EmoMusic show that this approach outperforms state‑of‑the‑art models, including the top MediaEval 2021 entry.
arXiv:2606. 17126v1 Announce Type: cross Abstract: Singing style is a crucial aspect of a natural and expressive singing voice.
arXiv:2606. 07309v1 Announce Type: cross Abstract: Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw audio is already available.
arXiv:2607. 16803v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) is an important component in a wide range of human-centered applications, including healthcare, customer service, and human-omputer interaction.
arXiv:2606. 10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals.
arXiv:2606. 26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations.
arXiv:2606. 00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues.
arXiv:2605. 27944v2 Announce Type: replace Abstract: With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical.
The paper presents a convolutional neural network (CNN) model for speech emotion recognition (SER), aiming to classify emotions from audio recordings. It evaluates the model using precision, recall, and F1 score, and explores how various input and output parameters influence performance. The study applies the SER system to digital healthcare, proposing it as a bridge between human emotional states and artificial intelligence.
arXiv:2607. 26698v1 Announce Type: cross Abstract: Cover song generation (CSG) should preserve the melodic and linguistic content of a reference song while recreating the remaining musical components.
arXiv:2608.30823v1 Announce Type: cross Abstract: The vocal music of each language carries a distinctive sonic identity, even without instrumental accompaniment. We ask whether these differences are...