The different facets of timbre: data-driven modelling of musical instruments sounds perception
Bibliographic record
Abstract
Although extensively studied for many years, defining the timbre of musical sounds remains unclear and somewhatcontroversial. We here address this question by using representations of sounds inspired by auditory cortical processes - so-called spectro-temporal modulation representations - as front-end representations to interpretable metric learning techniques modelling human dissimilarity ratings. We present a meta-analysis of 17 published experiments on the perception of musical instrument timbre. The results reveal that these studies are only partly replicable. Interestingly, we observed that spectro-temporal modulations embed relevant information to model human dissimilarity ratings of musical instruments sounds. Thanks to an interpretable distance metric learning technique, the results strikingly suggest that humans use both generic and context-driven acoustical cues defining the different facets of musical instrument timbre. This study hereby provides a unique overview of 17 historical studies on timbre and points the limitation of the traditional dimensional analyses. We further propose a new way to investigate the acoustical correlates of timbre. The proposed methodology hence opens avenues to link acoustical representations to high-level human judgements.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".