Perceptually-Motivated Sonification of Spatiotemporally-Dynamic CFD Data
Bibliographic record
Abstract
Everyday perception and action are fundamentally multisensory. Despite this, the sole reliance on visualization for the representation of complex 3D spatiotemporal data is still widespread. In the past we have proposed various prototypes for the sonification of dense data from computational fluid dynamics (CFD) simulations of turbulent-like blood flow, but did not robustly consider the perception and associated meaning-making of the resultant sounds. To reduce some of the complexities of these data for sonification, in this work we present a feature-based approach, applying ideas from auditory scene analysis to sonify different data features along perceptually-separable auditory streams. As there are many possible features in these dense data, we followed the analogy of “caricature” to guide our definition and subsequent amplification of unique spectral and fluctuating features, while effectively minimizing the features common between simulations. This approach may allow for better insight into the behavior of flow instabilities when compared to our previous sonifications and/or visualizations, and additionally we observed benefits when some redundancy was maintained between modalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".