Sonification of Emotion: Strategies and results from the intersection with music
Bibliographic record
Abstract
Emotion is a word not often heard in sonification, though advances in affective computing make the data type imminent. At times the relationship between emotion and sonification has been contentious due to an implied overlap with music. This paper clarifies the relationship, demonstrating how it can be mutually beneficial. After identifying contexts favourable to auditory display of emotion, and the utility of its development to research in musical emotion, the current state of the field is addressed, reiterating the necessary conditions for sound to qualify as a sonification of emotion. With this framework, strategies for display are presented that use acoustic and structural cues designed to target select auditory-cognitive mechanisms of musical emotion. Two sonifications are then described using these strategies to convey arousal and valence though differing in design methodology: one designed ecologically, the other computationally. Each model is sampled at 15-second intervals at 49 evenly distributed points on the AV space, and evaluated using a publically available tool for computational music emotion recognition. The computational design performed 65 times better in this test, but the ecological design is argued to be more useful for emotional communication. Conscious of these limitations, computational design and evaluation is supported for future development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".