Koechlin’s <i>volume</i> : Perception of sound extensity among instrument timbres from different families
Bibliographic record
Abstract
Charles Koechlin’s Traité de l’orchestration ascribes different dimensions to timbre than those usually discussed in multidimensional scaling studies: “volume” or grosseur, related to extensity (the sound’s perceived size), and intensity, related to loudness. Koechlin also provides volume rankings for orchestral instruments in different registers. Studies show that humans, as well as several animal species, perceive extensity for many sound sources, but none has demonstrated its relevance for musical instruments from different families. To test extensity, samples of seven orchestral instruments equalized in pitch, but not in loudness, were used. Task 1 required participants to order eight sets of samples on a largeness (grosseur) scale from “less large” ( moins gros) to “larger” ( plus gros). Task 2 required them to quantify the sounds’ largeness compared to a reference sample on a ratio scale. Both studies show that participants share a common extensity perception for instrument timbres of different families that is very similar to Koechlin’s proposed scale. This perception seems to be related to spectral shape and particularly to acoustic energy in the lower frequencies. Perception of this attribute is unrelated to musical training, native language, and the presence of minor hearing loss, which suggests that extensity could be a universal attribute of timbre perception that is useful in orchestration practice and theory.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".