TimbreFields: 3D Interactive Sound Models for Real-Time Audio
Bibliographic record
Abstract
We describe a methodology for virtual reality designers to capture and resynthesize the variations in sound made by objects when we interact with them through contact such as touch. The timbre of contact sounds can vary greatly, depending on both the listener’s location relative to the object, and the interaction point on the object itself. We believe that an accurate rendering of this variation greatly enhances the feeling of immersion in a simulation. To do this, we model the variation with an efficient algorithm based on modal synthesis. This model contains a vector field that is defined on the product space of contact locations and listening positions around the object. The modal data are sampled on this high dimensional space using an automated measuring platform. A parameter-fitting algorithm is presented that recovers the parameters from a large set of sound recordings around objects and creates a continuous timbre field by interpolation. The model is subsequently rendered in a real-time simulation with integrated haptic, graphic, and audio display. We describe our experience with an implementation of this system and an informal evaluation of the results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".