Sensor Choice for Parameter Modulations in Digital Musical Instruments: Empirical Evidence from Pitch Modulation
Bibliographic record
Abstract
This paper describes ongoing research into the design of new digital musical instruments (DMIs). While many new DMIs have been created using a variety of sensors, there has been relatively little empirical research into determining the optimal choice of sensor for control of specific musical functions. In this paper we attempt to identify an optimal choice of sensor for the control of parameter modulations in a DMI. Two experiments were conducted. In the first, pianists and violinists were tested on three strategies for producing pitch modulations. Both subjective user ratings and objective performance scores were analysed. The results suggest that modulated applied pressure is the optimal control for pitch modulation. Preference and performance did not appear to be directly mediated by previous musical experience. In the second experiment, the accuracy, stability and depth of modulation were measured for a number of musicians performing modulations with each of three control strategies. Results indicate that some options offer improved stability or accuracy over others and that performance with all strategies is significantly dependent on the speed of modulation. Overall results show that the optimal choice of sensor should be based on a combination of subjective user preference ratings and objective performance measurements.
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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 | 0.008 | 0.080 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".