Towards Characterising Induced Emotions: Exploiting Physiological Data and Investigating the Effect of Music Familiarity
Résumé
Music recommendation aims to suggest songs that align with listeners' preferences. Recent work has shown a strong correlation between induced emotion, i.e., the emotion actually felt during music listening, and music appreciation. However, the potential of leveraging induced emotions for music recommendation still remains unexplored. This paper explores the use of physiological data and music features to predict discrete emotional responses, as defined by the Geneva Emotional Music Scale (GEMS) model. Our results show that integrating physiological data and music familiarity enhances prediction accuracy compared to integrating music features only. Additionally, feature importance analysis revealed that, although music features remained the primary predictor of induced emotions, physiological data contributed substantially to the prediction model.
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