Acoustic and musical features of emotional response to orchestral gestures
Bibliographic record
Abstract
Recent empirical research indicates the impact of prominent changes in instrumentation on the listening experience: several studies suggest that timbral changes evoke music-induced emotions. However, orchestration remains an underdeveloped area of music theory. A model of orchestral gestures defined by changes in instrumentation in terms of time course (gradual or sudden) and direction (addition or reduction) is presented. An exploratory behavioral study that tested the perceptual relevance of orchestral gestures on listeners’ continuous ratings of emotional intensity was conducted. We demonstrate a new type of visualization that illustrates the relative textural density of each instrument family over time combined with other time-varying parameters extracted from the signal (loudness, spectral centroid, tempo, and roughness) and calculated from the score (instrumental texture, melodic range, and attack density). In addition to quantitative and qualitative comparison of similar orchestral gestures across pieces, we use this method to study the interaction of specific instrumentation changes and other musical parameters. Through discussion of the visualizations, we highlight relationships between the perceptual and musical/acoustical dimensions and quantify elements of the temporality of these experiences.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".