Proof of Concept of a Generic Toolkit for Sonification: The Sonification Cell in Ossia Score
Bibliographic record
Abstract
A popular topic in sonification research is the development of a complete, user-friendly tool for sonification creation. A study of existing attempts at such tools highlights what seems to be the main challenge of this endeavor: exhaustiveness with regards to the great diversity of approaches for auditory display of information. Most tools are designed to allow for selecting among a few typical modalities, but could not really be used to create any kind of sonification. In order to tackle this issue, we proposed a theoretical model of the sonification process, which is intended to take all of its properties as a data observation technique into account. The goal for this model is to be translated into a user interface and programming approach as part of a sonification toolkit. In the present paper, we report our work in creating a proof of concept of such a toolkit using the ossia musical sequencing environment, chosen for its proximity to our objectives in terms of user interaction and library of functionalities. This prototype was tested to recreate two of our previous data sonification works. Most of the specificities of these case studies could be recreated properly, though some of the planned features, notably for grain synthesis, are currently missing from the ossia environment. For our future works, we will consider that the current state of this proof of concept is sufficient to start studying the user experience of sonification designers interacting with the toolkit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".