Continuous Response to Perceived Affective Intentions in Music Listening
Bibliographic record
Abstract
Perception of affective intentions in music is a complex, yet commonplace, phenomenon that is influenced by listeners’ experience with different musical traditions and the style of the music. This study explores the continuous responses of listeners to a piece of Chinese orchestral music and how their musical backgrounds influence affective responses, as well as the acoustic and musical features used in the perception of affective intentions. Three groups of listeners (trained in Chinese and Western art music traditions and nonmusicians) were presented with a 15.5-minute piece of music and responded continuously on a unidimensional emotional intensity scale and a two-dimensional valence and arousal interface in separate blocks. Functional data analysis compared differences between listener groups’ response profiles. Time series analysis explored how each listener group utilized different acoustic and musical features over the course of the music. Results show significant differences between listener groups over different sections of the music. Valence responses diverge more than arousal or emotional intensity responses. The perception of affective intentions in music is influenced by the degree of familiarity listeners have with a musical tradition, the content implicated in the music, and the complex sonic environment created by the composer’s creation and the musicians’ interpretation.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".