A Cross-Cultural Analysis of the Influence of Timbre on Affect Perception in Western Classical Music and Chinese Music Traditions
Bibliographic record
Abstract
Timbre is one of the psychophysical cues that has a great impact on affect perception, although, it has not been the subject of much cross-cultural research. Our aim is to investigate the influence of timbre on the perception of affect conveyed by Western and Chinese classical music using a cross-cultural approach. Four listener groups (Western musicians, Western nonmusicians, Chinese musicians, and Chinese nonmusicians; 40 per group) were presented with 48 musical excerpts, which included two musical excerpts (one piece of Chinese and one piece of Western classical music) per affect quadrant from the valence-arousal space, representing angry, happy, peaceful, and sad emotions and played with six different instruments (erhu,dizi,pipa, violin, flute, and guitar). Participants reported ratings of valence, tension arousal, energy arousal, preference, and familiarity on continuous scales ranging from 1 to 9. ANOVA reveals that participants’ cultural backgrounds have a greater impact on affect perception than their musical backgrounds, and musicians more clearly distinguish between a perceived measure (valence) and a felt measure (preference) than do nonmusicians. We applied linear partial least squares regression to explore the relation between affect perception and acoustic features. The results show that the important acoustic features for valence and energy arousal are similar, which are related mostly to spectral variation, the shape of the temporal envelope, and the dynamic range. The important acoustic features for tension arousal describe the shape of the spectral envelope, noisiness, and the shape of the temporal envelope. The explanation for the similarity of perceived affect ratings between instruments is the similar acoustic features that were caused by the physical characteristics of specific instruments and performing techniques.
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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 | 0.002 |
| 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 | 0.001 |
| 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".