Perceiving Musical Individuality: Performer Identification is Dependent on Performer Expertise and Expressiveness, but Not on Listener Expertise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Can listeners distinguish unfamiliar performers playing the same piece on the same instrument? Professional performers recorded two expressive and two inexpressive interpretations of a short organ piece. Nonmusicians and musicians listened to these recordings and grouped together excerpts they thought had been played by the same performer. Both musicians and nonmusicians performed significantly above chance. Expressive interpretations were sorted more accurately than inexpressive ones, indicating that musical individuality is communicated more efficiently through expressive performances. Furthermore, individual performers' consistency and distinctiveness with respect to expressive patterns were shown to be excellent predictors of categorisation accuracy. Categorisation accuracy was superior for prize-winning performers compared to non-winners, suggesting a link between performer competence and the communication of musical individuality. Finally, results indicate that temporal information is sufficient to enable performer recognition, a finding that has broader implications for research on the detection of identity cues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it