Human dissimilarity ratings of musical instrument timbre: A computational meta-analysis
Bibliographic record
Abstract
Musical instrument timbre has been intensively investigated through dissimilarity rating tasks. It is now well known that audio descriptors such as attack time and spectral centroid, among others, account well for the dimensions of the timbre spaces underlying these dissimilarity ratings. Nevertheless, it remains very difficult to reproduce these perceptual judgments from distances computed on acoustical representations such as the waveform or the spectrogram. Interestingly, biologically inspired representations based on spectrotemporal modulation spectra such as spectrotemporal receptive fields (STRF) have been shown to be well-suited to reproduce human dissimilarity ratings (Patil et al., 2012). Here, we propose a meta-analysis of seven former studies on timbre spaces in light of these recently developed representations. We implemented a computational framework that optimizes the correlation between the perceptual results and distances obtained from a set of different acoustic representations, in particular through the STRF. We observed that distances computed from spectrotemporal modulation representations provide the best correlation with the perceptual results across the seven timbre spaces. Finally, we highlighted the parts of the representations contributing the most to the correlation suggesting new insights into the underlying perceptual metrics. [Supported by Canada Research Chair, NSERC (RGPIN-2015-05208, RGPAS-478121-15), (RGPIN-262808-2012), and EU MSCf (Project MIM, H2020-MSCA-IF-2014, GA no. 659).]
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".