Exploring emotional responses to orchestral gestures
Bibliographic record
Abstract
Research on emotional responses to music indicates that prominent changes in instrumentation and timbre elicit strong responses in listeners. However, there are few theories related to orchestration that would assist in interpreting these empirical findings. This article investigates listeners’ emotional responses to four types of orchestral gestures – large-scale timbral and textural changes that occur in a coordinated, goal-directed manner – through an exploratory experiment that collected continuous responses of emotional intensity for musician and nonmusician listeners. A time series regression analysis was used to predict changes in emotional responses by modeling changes in several musical features, including instrumental texture, spectral centroid, loudness, and tempo. We demonstrate the application of a new visualization tool that compiles the emotional intensity ratings with score-based and performance-based musical features for qualitative and quantitative analysis. The results suggest that the response profiles differ for the four gestural types. Following the increasing growth of instrumental texture and loudness, the emotional intensity ratings climbed steadily for the gradual addition types. The ratings for the sudden addition gestures sharply increased in response to the rapid textural change, peaking toward the end of the excerpt. There was a slight tendency for musicians, but not nonmusicians, to anticipate the moment of sudden addition with heightened emotional responses. The responses to the reductive excerpts, both gradual and sudden, feature a plateau of lingering high emotional intensity, despite the decrease of other musical features. The visualization provided a method to observe the evolution of listeners’ emotional reactions in response to the orchestral gestures and assisted in interpreting the results of the time series regression analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".