Using Circular Models to Improve Music Emotion Recognition
Why this work is in the frame
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Bibliographic record
Abstract
The two commonly accepted models of affect used in affective computing are categorical and two-dimensional. However, categorical models are limited to datasets that only contain music for which human annotators fully agree upon, while two-dimensional models use descriptors to which users may not relate to (e.g., Valence and Arousal). This paper explores the hypothesis that the music emotion problem is circular, and shows how circular models can be used for automatic music emotion recognition. This hypothesis is tested through experiments on the two commonly accepted models of affect, as well as on an original circular model proposed by the authors. First, an original dataset was assembled and annotated as a way to investigate agreement among annotators. Then, polygonal approximations of circular regression are proposed as a practical method to investigate whether the circularity of the annotations can be exploited. Experiments with different polygons demonstrate consistent improvements over the categorical model on a dataset containing musical extracts for which the human annotators did not fully agree upon. Finally, a proposed multi-tagging strategy based on the circular predictions is put forward as a pragmatic method to automatically annotate music based on the circular models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it