Blending Between Bassoon and Horn Players
Bibliographic record
Abstract
Achieving a blended timbre between two instruments is a common aim of orchestration. It relates to the auditory fusion of simultaneous sounds and can be linked to several acoustic factors (e.g., temporal synchrony, harmonicity, spectral relationships). Previous research has left unanswered if and how musicians control these factors during performance to achieve blend. For instance, timbral adjustments could be oriented towards the leading performer. In order to study such adjustments, pairs of one bassoon and one horn player participated in a performance experiment, which involved several musical and acoustical factors. Performances were evaluated through acoustic measures and behavioral ratings, investigating differences across performer roles as leaders or followers, unison or non-unison intervals, and earlier or later segments of performances. In addition, the acoustical influence of performance room and communication impairment were also investigated. Role assignments affected spectral adjustments in that musicians acting as followers adjusted toward a “darker” timbre (i.e., realized by reducing the frequencies of the main formant or spectral centroid). Notably, these adjustments occurred together with slight reductions in sound level, although this was more apparent for horn than bassoon players. Furthermore, coordination seemed more critical in unison performances and also improved over the course of a performance. These findings compare to similar dependencies found concerning how performers coordinate their timing and suggest that performer roles also determine the nature of adjustments necessary to achieve the common aim of a blended timbre.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".