Modal active control: A tool to finely adjust the sound of string instruments
Bibliographic record
Abstract
Controlling the vibratory properties of musical instruments is an important challenge for musical acousticians, musicians, and instrument makers. The latter try to control these properties modifying mechanical parameters of instruments like their shape or their materials to obtain some expected sound attributes. Musicians also modify the vibratory properties of their instruments to change their sound using mutes, for example. Musical acousticians try to modify these properties because it is an intuitive way to investigate the relationship between the instrument mechanisms and their sound attributes. Inspired by industrial techniques, active control was revealed as a convenient way to answer to the latter goal. Moreover, modal active control is a preferred method for the application to musical instruments since their modal parameters are believed to be good descriptors of their vibratory properties. This study aims at applying modal active control on string instruments. First, the possibilities offered by this technique are presented and experimented on several instruments. Then, examples of the control of specific phenomena are given. Couplings between the soundboard and strings for both a cello and a guitar are controlled to cancel the well-known wolf-note phenomenon for the former and to switch from strong to weak coupling for the latter.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".