Timbre Semantic Associations Vary Both Between and Within Instruments
Bibliographic record
Abstract
The main objective of this study is to understand how timbre semantic associations—for example, a sound’s timbre perceived as bright, rough, or hollow—vary with register and pitch height across instruments. In this experiment, 540 online participants rated single, sustained notes from eight Western orchestral instruments (flute, oboe, bass clarinet, trumpet, trombone, violin, cello, and vibraphone) across three registers (low, medium, and high) on 20 semantic scales derived from Reymore and Huron (2020). The 24 two-second stimuli, equalized in loudness, were produced using the Vienna Symphonic Library. Exploratory modeling examined relationships between mean ratings of each semantic dimension and instrument, register, and participant musician identity (“musician” vs. “nonmusician”). For most semantic descriptors, both register and instrument were significant predictors, though the amount of variance explained differed (marginal R2). Terms that had the strongest positive relationships with register include shrill/harsh/noisy, sparkling/brilliant/bright, ringing/long decay, and percussive. Terms with the strongest negative relationships with register include deep/thick/heavy, raspy/grainy/gravelly, hollow, and woody. Post hoc modeling using only pitch height and only register to predict mean semantic rating suggests that pitch height may explain more variance than does register. Results help clarify the influence of both instrument and relative register (and pitch height) on common timbre semantic associations.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".