Predicting Emotion from Music Audio Features Using Neural Networks
Bibliographic record
Abstract
<p>We describe our implementation of two neural networks: a static feedforward network, and an Elman network, for predicting mean valence/arousal ratings of participants for musical excerpts based on audio features. Thirteen audio features were extracted from 12 classical music excerpts (3 from each emotion quadrant). Valence/arousal ratings were collected from 45 participants for the static network, and 9 participants for the Elman network. For the Elman network, each excerpt was temporally segmented into four, sequential chunks of equal duration. Networks were trained on eight of the 12 excerpts and tested on the remaining four. The static network predicted values that closely matched mean participant ratings of valence and arousal. The Elman network did a good job of predicting the arousal trend but not the valence trend. Our study indicates that neural networks can be trained to identify statistical consistencies across audio features to predict valence/arousal values.</p>
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".