Mapping strategies for sound synthesis, digital audio effects, and sonification of performer gestures
Bibliographic record
Abstract
Mapping strategies are an essential step when designing realtime musical performance systems, as well as offline digital sound processing. These strategies define how we relate input device parameters to sound synthesis or audio effect parameters. This implies the ability to combine input parameters among themselves (parameter combination) and valid control signals in terms of range, variation type, etc. (signal conditioning). Recent works highlighted the interest of multi-layer mapping strategies in the context of digital musical instruments, which can also be applied in the context digital audio effects. In this presentation, three strategies will be discussed in order to illustrate the role of mapping strategies in various contexts. The first example concerns an additive synthesizer called Ssynth, a further development of Escher, a prototyping system aiming at studying the effect of mapping strategy in instrument design. The second example is a general mapping strategy for digital audio effects, allowing for both adaptive and gestural control. The final example concerns sonification of gestures, used to provide cues about ancillary movements of performers. For each example, mapping strategies will be explained in terms of their structure and functionality. [Work supported by FQRNT and MDEIE PSR-SIIRI (Québec, Canada), CNRS and PACA (France).]
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".