Interactive Sonification for Data Exploration: How listening modes and display purposes define design guidelines
Bibliographic record
Abstract
The desire to make data accessible through the sense of listening has led to ongoing research in the fields of sonification and auditory display since the early 1990s. Coming from the disciplines of computer sciences and human computer interface (HCI), the conceptualisation of sonification has been mostly driven by application areas and methods. On the other hand, the sonic arts, which have always participated in the auditory display community, have a genuine focus on sound. Despite these close interdisciplinary relationships between communities of sound practitioners, a rich and sound- or listening-centred concept of sonification is still missing for design guidelines. Complementary to the useful organisation by fields of application, a proper conceptual framework for sound needs to be abstracted from applications and also to some degree from tasks, as both are not directly related to sound. As an initial approach to recasting the thinking about sonification, we propose a conceptualisation of sonifications along two poles in which sound serves either anormativeor adescriptivepurpose. According to these two poles, design guidelines can be developed proper to display purposes and listening modes.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".