Acoustic and Categorical Dissimilarity of Musical Timbre: Evidence from Asymmetries Between Acoustic and Chimeric Sounds
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the role of acoustic and categorical information in timbre dissimilarity ratings. Using a Gammatone-filterbank-based sound transformation, we created tones that were rated as less familiar than recorded tones from orchestral instruments and that were harder to associate with an unambiguous sound source (Experiment 1). A subset of transformed tones, a set of orchestral recordings, and a mixed set were then rated on pairwise dissimilarity (Experiment 2A). We observed that recorded instrument timbres clustered into subsets that distinguished timbres according to acoustic and categorical properties. For the subset of cross-category comparisons in the mixed set, we observed asymmetries in the distribution of ratings, as well as a stark decay of inter-rater agreement. These effects were replicated in a more robust within-subjects design (Experiment 2B) and cannot be explained by acoustic factors alone. We finally introduced a novel model of timbre dissimilarity based on partial least-squares regression that compared the contributions of both acoustic and categorical timbre descriptors. The best model fit (R (2) = 0.88) was achieved when both types of descriptors were taken into account. These findings are interpreted as evidence for an interplay of acoustic and categorical information in timbre dissimilarity perception.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it