Towards Unified Music Emotion Recognition across Dimensional and Categorical Models
Quick summary
arXiv:2502.03979v3 Announce Type: replace-cross Abstract: One of the most significant challenges in Music Emotion Recognition (MER) comes from the fact that emotion labels can be heterogeneous across datasets with regard to the emotion representation, including categorical (e.g., happy, sad) versus dimensional labels (e.g., valence-arousal). In this paper, we present a unified multitask learning framework that combines these two types of labels and is thus able to be trained on multiple datasets. This framework uses an effective input representation that combines musical features (i.e., key an
Key takeaways
- arXiv:2502.03979v3 Announce Type: replace-cross Abstract: One of the most significant challenges in Music Emotion Recognition (MER) comes from the fact that emotion labels can be heterogeneous across datasets with regard to the emotion representation, including categorical (e.g., happy, sad) versus dimensional labels (e.g., valence-arousal).
- In this paper, we present a unified multitask learning framework that combines these two types of labels and is thus able to be trained on multiple datasets.
- This framework uses an effective input representation that combines musical features (i.e., key an
Why it matters
“Towards Unified Music Emotion Recognition across Dimensional and Categorical Models” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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