Perceptual evaluation of violins: A comparison of intra-individual agreement in playing vs. listening tasks for the case of richness
Bibliographic record
Abstract
In previous studies by the authors, it was shown that there is a significant lack of agreement between violinists when evaluating different instruments in terms of perceived richness in free-playing tasks. A new experiment was designed to further investigate the perceptual evaluation of richness using both a constrained-playing task, which was recorded, and a subsequent listening task (using the previously recorded sounds). The goal was to compare the evaluation of richness from playing vs. listening tasks in order to better understand whether they are based more on auditory feedback or tactile and proprioceptive cues in the wider context of correlating audio features extracted from the recordings with richness judgments. Skilled violinists were asked to rank five different instruments by playing only certain notes on the G-string. Subsequently, the players were asked to listen to their recordings and rank the violins. Results appeared to show a higher inter-individual agreement relative to previous studies. Furthermore, the rankings in the playing task were generally different from those in the listening task, indicating that the evaluation of richness is based on different criteria in the two cases. Results from matching the tristimulus and spectral centroid of recorded tones to richness judgments will be presented at the conference.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".