Time-Frequency Scattering Accurately Models Auditory Similarities\n Between Instrumental Playing Techniques
Bibliographic record
Abstract
Instrumental playing techniques such as vibratos, glissandos, and trills\noften denote musical expressivity, both in classical and folk contexts.\nHowever, most existing approaches to music similarity retrieval fail to\ndescribe timbre beyond the so-called "ordinary" technique, use instrument\nidentity as a proxy for timbre quality, and do not allow for customization to\nthe perceptual idiosyncrasies of a new subject. In this article, we ask 31\nhuman subjects to organize 78 isolated notes into a set of timbre clusters.\nAnalyzing their responses suggests that timbre perception operates within a\nmore flexible taxonomy than those provided by instruments or playing techniques\nalone. In addition, we propose a machine listening model to recover the cluster\ngraph of auditory similarities across instruments, mutes, and techniques. Our\nmodel relies on joint time--frequency scattering features to extract\nspectrotemporal modulations as acoustic features. Furthermore, it minimizes\ntriplet loss in the cluster graph by means of the large-margin nearest neighbor\n(LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we\nreport a state-of-the-art average precision at rank five (AP@5) of\n$99.0\\%\\pm1$. An ablation study demonstrates that removing either the joint\ntime--frequency scattering transform or the metric learning algorithm\nnoticeably degrades performance.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".