A Meta-Analysis of Music Emotion Recognition Studies
Bibliographic record
Abstract
This meta-analysis examines music emotion recognition (MER) models published between 2014 and 2024, focusing on predictions of valence, arousal, and categorical emotions. A total of 553 studies were identified, of which 96 full-text articles were assessed, resulting in a final review of 34 studies. These studies reported 204 models, including 86 for emotion classification and 204 for regression. Using the best-performing model from each study, we found that valence and arousal were predicted with reasonable accuracy (r = 0.67 and r = 0.81, respectively), while classification models achieved an accuracy of 0.87 as measured with Matthews correlation coefficient. Across modelling approaches, linear and tree-based methods generally outperformed neural networks in regression tasks, whereas neural networks and support vector machines (SVMs) showed highest performance in classification tasks. We highlight key recommendations for future MER research, emphasizing the need for greater transparency, feature validation, and standardized reporting to improve comparability across studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".