On the relation between gesture, tone production, and perception in classical cello performance
Bibliographic record
Abstract
On bowed string instruments such as violin or cello, the quality of sound depends mostly on the performer’s bowing technique, which determines the interaction between the bow hair and the string. An accomplished string player has numerous ways of shaping the spectrum of a desired sound. This research investigates the combination of bowing gestures necessary for production of a rich tone. In particular, bowing control parameters such as bow force, bow velocity, and bow–bridge distance captured by a dedicated sensing system are analyzed and compared against audio features. Using audio and gesture measurements of six advanced cellists recorded on two different instruments of a luthier class, we characterize a sound palette and respective bowing control patterns of each player in performed music excerpts and scales. We especially focus on how performers adjust their bowing technique to control the timber of an instrument on which they have never practiced before. Observed differences between the players on the measured audio features show consistency with the bowing parameters adapted for balancing the timbral changes due to instrument, string, and fingering position. To perceptually evaluate the recorded samples, expert musicians were asked to rank the players in terms of sound quality and tone richness.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".