Bibliographic record
Abstract
Generating realistic sounds for soft bodies is a challenging task due to the complexity of the interactions. Therefore, automatic audio generation based on procedural approach has become an attractive method for digital synthesis of soft-body sounds. In this paper, we present a comprehensive review in the field of procedural audio, with a special focus on synthesizing the sound of soft bodies. We first introduce the concept of procedural sound generation, including its advantages and challenges for soft-body sound synthesis. Next, we review the state-of-the-art in rigid/non-rigid-body sound synthesis techniques for computer animations and games. Thirdly, we summarize and survey a taxonomy of existing synthesis methods from previous literatures by analyzing their benefits and drawbacks for generating soft-body sounds. These methods include modal synthesis, sound texture modeling, motion-driven sound synthesis, wavelet tree learning, granular synthesis, and concatenative sound synthesis. Last but not least, we discuss several possible directions for future research in procedural soft-body sound synthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".