Bibliographic record
Abstract
Annotating music poses a cognitive load on listeners and this potentially interferes with the emotions being reported. One solution is to let software agents learn to make the annotator’s task easier and more efficient. Emo is a music annotation prototype that combines inputs from both human and software agents to better study human listening. A compositional theory of musical meaning provides the overall heuristics for the annotation process, with the listener drawing upon different influences such as acoustics, lyrics and cultural metadata to focus on a specific musical mood. Software agents track the way these choices are made from the influences available. A functional theory of human emotion provides the basis for introducing necessary bias into the machine learning agents. Conflicting positive and negative emotions can be separated on the basis of their different function (reward-approach and threat-avoidance) or dysfunction (psychotic). Negative emotions have strong ambiguity and these are the focus of the experiment. The results of mining psychological features of lyrics are promising, recognisable in terms of common sense ideas of emotion and in terms of accuracy. Further ideas for deploying agents in this model of music annotation are presented.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".