Perception of touch quality in piano tones
Bibliographic record
Abstract
Both timbre and dynamics of isolated piano tones are determined exclusively by the speed with which the hammer hits the strings. This physical view has been challenged by pianists who emphasize the importance of the way the keyboard is touched. This article presents empirical evidence from two perception experiments showing that touch-dependent sound components make sounds with identical hammer velocities but produced with different touch forms clearly distinguishable. The first experiment focused on finger-key sounds: musicians could identify pressed and struck touches. When the finger-key sounds were removed from the sounds, the effect vanished, suggesting that these sounds were the primary identification cue. The second experiment looked at key-keyframe sounds that occur when the key reaches key-bottom. Key-bottom impact was identified from key motion measured by a computer-controlled piano. Musicians were able to discriminate between piano tones that contain a key-bottom sound from those that do not. However, this effect might be attributable to sounds associated with the mechanical components of the piano action. In addition to the demonstrated acoustical effects of different touch forms, visual and tactile modalities may play important roles during piano performance that influence the production and perception of musical expression on the piano.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".