Towards Predicting Expressed Emotion in Music from Pairwise Comparisons |
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| Abstract | We introduce five different regression models for the modeling of expressed emotion in music using data obtained in a two alternative forced choice listening experiment. The predictive performance of the proposed models is compared using learning curves, showing that all models converge to produce a similar classification error. The predictive ranking of the models is compared using Kendall's tau rank correlation coefficient and shows a difference despite similar classification error. The variation in predictions across subjects and the difference in ranking is investigated visually in the AV space and quantified using Kendall's tau. |
| Keywords | expressed emotion, pairwise comparison, Gaussian process, Generalized Linear models |
| Type | Conference paper [With referee] |
| Conference | 9th Sound and Music Computing Conference (SMC) Illusions |
| Year | 2012 Month July |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |