When timbre blends musically: perception and acoustics underlying orchestration and performance
Bibliographic record
Abstract
Blending or contrasting instrumental timbres are common techniques employed in orchestration. Both bear a direct relevance to the perceptual phenomenon of auditory fusion, which in turn depends on a series of acoustical cues. Whereas some cues relate to musical aspects, such as timing and pitch relationships, instrumentation choices more likely concern the acoustical traits of instrument timbre. Apart from choices made by composers and orchestrators, the success of timbre blending still depends on precise execution by musical performers, which argues for its relevance to musical practice as a whole.This thesis undertakes a comprehensive investigation aiming to situate timbre blend in musical practice, more specifically addressing the perceptual effects and acoustical factors underlying both orchestration and performance practice. Three independent studies investigated the perception of blend as a function of factors related to musical practice, i.e., those derived from musical context and realistic scenarios (e.g., pitch relationships, leadership in performance, room acoustics).The first study establishes generalized spectral descriptions for wind instruments, which allow the identification of prominent features assumed to function as their timbral signatures. Two listening experiments investigate how these features affect blend by varying them in frequency, showing a critical perceptual relevance. The second study considers two other listening experiments, which evaluate perceived blend for instrument combinations in dyads and triads, respectively. Correlational analyses associate the obtained blend measures with a wide set of acoustic measures, showing that blend depends on pitch and temporal relationships as well as the previously identified spectral features. The third study extends the previous ones, addressing factors related to musical performance by investigating the timbral adjustments performers employ in blending with one another, as well as their interactive relationship. Timbral adjustments can be shown to be made towards the musician leading the performance.All studies contribute to a greater understanding of blend as it applies to musical and orchestration practice. Their findings expand previous research and provide possible explanations for discrepancies between hypotheses made in the past. Together, the conclusions drawn allow us to propose a general perceptual theory for timbre blend as it applies to musical practice, which considers the musical material and spectral relationships among instrument timbres as the determining factors.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".