Subjective and Objective Evaluation of Procedurally-Generated Audio for Soft-Body Interactions
Bibliographic record
Abstract
Procedurally-generated audio has proven to be an effective solution to synthesize complex sound phenomena such as soft-body interactions in computer animations and games. However, the quality of synthesized audio varies depending on the kind of methods or parameters chosen. As a consequence, it is often necessary to constantly evaluate the output sound quality as it is produced, which can be a difficult task. In this paper, we address this issue by taking both subjective and objective approaches, and with a focus on synthesized soft-body audio. In particular, our subjective evaluation consists of a three-part perceptual study, where we explore the recognisability, quality, and synchronization of the simulated sound. For objective evaluation, we adapt the metrics from generative adversarial networks (GANs) that also measure the recognisability as well as quality of the sound from a different angle. Our results suggest that while both evaluation criteria are largely independent of each other in assessing the recognisability of the sound, objective evaluation tends to be a more efficient alternative to measure the output sound quality. In addition, we provide several findings from our results that can guide sound designers in synthesizing higher quality audio for soft bodies.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".