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arXiv AI By Jaeyong Kang, Dorien Herremans

Towards Unified Music Emotion Recognition across Dimensional and Categorical Models

Read the original on arXiv AI →

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.

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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