Investigation of timbre saliency, the attention-capturing quality of timbre
Bibliographic record
Abstract
Timbre saliency is defined as the attention-capturing quality of timbre. Saliency differences between timbres were measured using a tapping technique in which the stronger beat in ABAB isochronous sequences was reproduced by the listener, the idea being that the more salient timbre would capture listeners' attention and be chosen more often as the strong beat. A timbre saliency space was defined in which the distance between a pair of timbres corresponded to the difference in timbre saliency. Stimuli were generated with 15 orchestral instruments, equalized in pitch, loudness and duration. Data from 40 participants yielded a one-dimensional CLASCAL solution with two latent classes and specificities. Latent class structure shows no relation with gender, musicianship or age. Testing audio descriptors from the Timbre Toolbox [Peeters et al., 2011, J. Acoust. Soc. Am., 130, 2902-2916], the odd-even harmonic energy ratio explains 51% of the variance along this dimension. A combination of tristimulus (band 3) and odd-even ratio explains 73% of the variance in the mean saliencies of individual sounds across all other comparison sounds. Mean saliency thus seems to depend on the high-frequency harmonic energy and spectral envelope jaggedness, whereas saliency comparisons between timbres depend more on spectral envelope jaggedness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".