Ordinal scaling of temporal audio descriptors and perceptual significance of attack temporal centroid in timbre spaces
Bibliographic record
Abstract
Temporal audio features play an important role in timbre perception and sound identification. An experiment was conducted to test whether listeners are able to rank order synthesized stimuli over a wide range of feature values restricted within the range of instrument sounds. The following audio descriptors were tested: attack and decay time, temporal centroid with fixed attack and decay time, and inharmonicity. The results indicate that these descriptors are susceptible to ordinal scaling. The spectral envelope played an important role when ordering stimuli with various inharmonicity levels, whereas the shape of the amplitude envelope was an important parameter when ordering stimuli with different attack and decay times. Linear amplitude envelopes made the ordering of attack times easier and caused the least amount of confusion among listeners, whereas exponential envelopes were more effective when ordering decay times. Although there were many confusions in ordering short attack and decay times, listeners performed well in ordering temporal centroids even at very short attack and decay times. A meta-analysis of six timbre spaces was therefore conducted to test the explanatory power of attack time versus the attack temporal centroid along a perceptual dimension. The results indicate that attack temporal centroid has greater overall explanatory power than attack time itself.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".