Bibliographic record
Abstract
Can listeners recognize the individual characteristics of unfamiliar performers playing two different musical pieces on the harpsichord? Six professional harpsichordists, three prize-winners and three non prize-winners, made two recordings of two pieces from the Baroque period (a variation on a Partita by Frescobaldi and a rondo by François Couperin) on an instrument equipped with a MIDI console. Short (8 to 15 s) excerpts from these 24 recordings were subsequently used in a sorting task in which 20 musicians and 20 non-musicians, balanced for gender, listened to these excerpts and grouped together those that they thought had been played by the same performer. Twenty-six participants, including 17 musicians and nine non-musicians, performed significantly better than chance, demonstrating that the excerpts contained sufficient information to enable listeners to recognize the individual characteristics of the performers. The grouping accuracy of musicians was significantly higher than that observed for non-musicians. No significant difference in grouping accuracy was found between prize-winning performers and non-winners or between genders. However, the grouping accuracy was significantly higher for the rondo than for the variation, suggesting that the features of the two pieces differed in a way that affected the listeners' ability to sort them accurately. Furthermore, only musicians performed above chance level when matching variation excerpts with rondo excerpts, suggesting that accurately assigning recordings of different pieces to their performer may require musical training. Comparisons between the MIDI performance data and the results of the sorting task revealed that tempo and, to a lesser extent, note onset asynchrony were the most important predictors of the perceived distance between performers, and that listeners appeared to rely mostly on a holistic percept of the excerpts rather than on a comparison of note-by-note expressive patterns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".