Perception of Material from Contact Sounds
Bibliographic record
Abstract
Contact sounds can provide important perceptual cues in virtual environments. We investigated the relation between material perception and variables that govern the synthesis of contact sounds. A shape-invariant, auditory-decay parameter was a powerful determinant of the perceived material of an object. Subjects judged the similarity of synthesized sounds with respect to material (Experiment 1 and 2) or length (Experiment 3). The sounds corresponded to modal frequencies of clamped bars struck at an intermediate point, and they varied in fundamental frequency and frequency-dependent rate of decay. The latter parameter has been proposed as reflecting a shape-invariant material property: damping. Differences between sounds in both decay and frequency affected similarity judgments (magnitude of similarity and judgment duration), with decay playing a substantially larger role. Experiment 2, which varied the initial sound amplitude, showed that decay rate—rather than total energy or sound duration—was the critical factor in determining similarity. Experiment 3 demonstrated that similarity judgments in the first two studies were specific to instructions to judge material. Experiment 4, in which subjects assigned the sounds to one of four material categories, showed an influence of frequency and decay, but confirmed the greater importance of decay. Decay parameters associated with each category were estimated and found to correlate with physical measures of damping. The results support the use of a simplified model of material in virtual auditory environments.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".