Variations in timbre qualia with register and dynamics in the oboe and French horn
Bibliographic record
Abstract
Many musical instruments produce a myriad of sound colors resulting from diverse playing techniques, both traditional and extended. Such techniques include parameters that are often regularly manipulated in music, such as pitch, intensity (dynamics), articulation style, and duration. Despite the likely contribution of such timbral variations to musical experience, within-instrument timbral flexibility and its semantic consequences have not been addressed empirically. Participants rated sounds produced by the oboe and the French horn on 12 combinations of register and dynamics using the 20-dimensional timbre qualia model from Reymore and Huron (2020). Data are modeled with Exploratory Factor Analysis, partial proportional odds regressions, and random forest classifiers. Although trends between ratings and register/dynamics emerged, the results illustrate the complexity of within-instrument timbral variability. Some trends were approximately linear, others demonstrated non-linear patterns, and some timbre qualia dimensions displayed interactions between register and dynamics. While certain trends were shared between the oboe and French horn, such as an increase in sparkling/brilliant ratings with register, others seem to be unique to each instrument, such as the relationship of ratings of woody to register for the oboe or of ratings of muted/veiled to dynamics for the horn. Results demonstrate that within-instrument timbral variability based on dynamic and register is apparent to listeners and that semantic interactions among parameters can be present. The methodology established in this paper can be extended to address within-instrument timbral flexibility with respect to articulation, duration, and other sources of variation for any instrument or group of instruments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".