The Acoustic Properties of Affective Timbres: Consistencies and Discrepancies in a Synthesis of Multiple Datasets
Bibliographic record
Abstract
In the investigation of musical features that influence musical affect, timbre has received relatively little attention. Investigating affective timbres as they vary between instrument families can lead to inconsistent results, because one instrument family can produce a wide variety of timbres. Here, we consider timbre descriptors, as fine-grained acoustic representations of a sound. Using identical methods, we re-analyzed and synthesized results from three previously published studies: Eerola et al. (2012, Mus. Percept.), McAdams et al. (2017, Front. Psychol.), and Korsmit et al. (2023, Front. Psychol.). In doing so, we aimed to reveal robust timbre descriptors that consistently predict the affective response and to explain any discrepancies in results arising from differences in experimental methodology. We computed spectral, temporal, and spectro-temporal descriptors from all stimuli and used these to predict the affect ratings using linear and nonlinear methods. Our most consistent finding was that the fundamental frequency or higher-frequency energy of a sound predicted pleasant affect (i.e., positive valence, happiness, sadness) in one direction and unpleasant affect (i.e., tension, anger, fear) in the opposite direction. Clear discrepancies in previous findings may be attributable to differences in experimental design. When pitch variation was present in a stimulus set, energy arousal was predicted by pitch and inharmonicity, whereas when attack variation was present in the stimulus set, energy arousal was predicted by a faster attack and shorter sustain.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".