On modelling emotional responses to rhythm features
Rhythm is one of the most essential elements of music that can easily capture the attention of the listener. In this study, we explored various rhythm features and used them to build emotion models. The emotion labels used are based on Thayers Model of Mood, which includes contentment, exuberance, a...
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2012
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oai:animorepository.dlsu.edu.ph:faculty_research-37002022-08-30T07:09:39Z On modelling emotional responses to rhythm features Cu, Jocelynn Cabredo, Rafael Legaspi, Roberto S. Suarez, Merlin Teodosia C. Rhythm is one of the most essential elements of music that can easily capture the attention of the listener. In this study, we explored various rhythm features and used them to build emotion models. The emotion labels used are based on Thayers Model of Mood, which includes contentment, exuberance, anxiety, and depression. Empirical results identify 11 low-level rhythmic features to classify music emotion. We also determined that KStar can be used to build user-specific emotion models with a precision value of 0.476, recall of 0.480, and F-measure of 0.475. © 2012 Springer-Verlag. 2012-10-25T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2701 Faculty Research Work Animo Repository Emotion recognition Musical meter and rhythm Computer Sciences |
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De La Salle University |
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Philippines Philippines |
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DLSU Institutional Repository |
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Emotion recognition Musical meter and rhythm Computer Sciences |
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Emotion recognition Musical meter and rhythm Computer Sciences Cu, Jocelynn Cabredo, Rafael Legaspi, Roberto S. Suarez, Merlin Teodosia C. On modelling emotional responses to rhythm features |
| description |
Rhythm is one of the most essential elements of music that can easily capture the attention of the listener. In this study, we explored various rhythm features and used them to build emotion models. The emotion labels used are based on Thayers Model of Mood, which includes contentment, exuberance, anxiety, and depression. Empirical results identify 11 low-level rhythmic features to classify music emotion. We also determined that KStar can be used to build user-specific emotion models with a precision value of 0.476, recall of 0.480, and F-measure of 0.475. © 2012 Springer-Verlag. |
| format |
text |
| author |
Cu, Jocelynn Cabredo, Rafael Legaspi, Roberto S. Suarez, Merlin Teodosia C. |
| author_facet |
Cu, Jocelynn Cabredo, Rafael Legaspi, Roberto S. Suarez, Merlin Teodosia C. |
| author_sort |
Cu, Jocelynn |
| title |
On modelling emotional responses to rhythm features |
| title_short |
On modelling emotional responses to rhythm features |
| title_full |
On modelling emotional responses to rhythm features |
| title_fullStr |
On modelling emotional responses to rhythm features |
| title_full_unstemmed |
On modelling emotional responses to rhythm features |
| title_sort |
on modelling emotional responses to rhythm features |
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Animo Repository |
| publishDate |
2012 |
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https://animorepository.dlsu.edu.ph/faculty_research/2701 |
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