Bibliographic record
Abstract
Procedural audio generation is an important method for automatically synthesizing realistic sounds for computer animations and games. While synthesis techniques for rigid bodies have been well studied, few existing works have tackled the challenges of soft-body interactions. In this dissertation, we explore practical methods for procedural audio generations of soft bodies. First, we synthesize both impulsive and continuous sounds for elastic deformations. Our method is built on granular synthesis that retargets sound tracks of real-world recordings to match the motion of input elastic objects. Next, to synthesize sounds for plastic deformations, we introduce a concatenative synthesis method with a fast feature correlation technique, which is able to calculate the motion events of highly deformable objects at run time and simultaneously generate sounds according to these motion signals. Last but not least, we focus on evaluating the synthesized audio using both subjective and objective techniques, and the results demonstrate that our proposed synthesis methods can produce convincing sounds for soft bodies that are comparable to the recorded ones. Our presented methods do not require computationally expensive physics simulations and have improved previous data-driven synthesis approaches with more efficient analysis, control and evaluation techniques, which makes it possible to automatically generate plausible audio for a variety of soft-body interactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".