Timbre saliency vs. timbre dissimilarity – What is the relationship?
Bibliographic record
Abstract
We have proposed the notion of timbre saliency as the attention-capturing quality of timbre. The definition of saliency requires an object to stand out with respect to its surroundings, implying dissimilarity between the object and its neighbors. What then might be the relationship between timbre saliency and timbre dissimilarity? A classic timbre dissimilarity experiment and a timbre saliency experiment were carried out with 20 participants on the same set of stimuli. Multidimensional scaling revealed a two-dimensional dissimilarity space. Using the features obtained from the Timbre Toolbox [Peeters et al., J. Acoust. Soc. Am. 130, 2902–2916 (2011)], the first dimension shows a high correlation with spectral centroid [r(13) = 0.845, p < 0.0001] and spectral spread [r(13) = 0.855, p < 0.0001], both based on the ERB-FFT model spectrum, and the second with the attack time [r(13) = -0.692, p = 0.004] and power spectral crest [r(13) = 0.732, p < .005]. This confirms spectral centroid and attack time as two major acoustic correlates of timbre dissimilarity. The saliency dimension shows a moderate correlation with the second dimension [r(13) = 0.578, p = 0.0241] but not with the first dimension [r(13) = 0.182, p = 0.517], suggesting that the saliency might be more related to the temporal characteristics of 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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".