Modeling Expressed Emotions in Music using Pairwise Comparisons |
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| Abstract | We introduce a two-alternative forced-choice experimental paradigm to quantify expressed emotions in music using the two well-known arousal and valence (AV) dimensions. In order to produce AV scores from the pairwise comparisons and to visualize the locations of excerpts in the AV space, we introduce a flexible Gaussian process (GP) framework which learns from the pairwise comparisons directly. A novel dataset is used to evaluate the proposed framework and learning curves show that the proposed framework needs relative few comparisons in order to achieve satisfactory performance. This is further supported by visualizing the learned locations of excerpts in the AV space. Finally, by examining the predictive performance of the user-specific models we show the importance of modeling subjects individually due to significant subjective differences. |
| Keywords | expressed emotion, pairwise comparison, Gaussian process |
| Type | Conference paper [With referee] |
| Conference | 9th International Symposium on Computer Music Modeling and Retrieval (CMMR) Music and Emotions |
| Year | 2012 Month June |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |