Fast Detection for Natural Animal Sounds
Bibliographic record
Abstract
Human listeners seem remarkably good at identifying complex and natural sounds. Relatively little attention, however, has been paid to the simple detection of such sounds. The present experiments measured the time course of sound detection in human listeners. Two categories of sounds were used: a train of 1-kHz pure tone pulses varying along an Inter Onset Interval dimension (termed here "IOI" sounds) and animal call sound samples. All sounds were equalized in loudness in a preliminary experiment. The task consisted of a simple reaction time, in which listeners were presented with interleaved IOI and animal sounds and had to manually press a button as fast as possible when they heard any stimulus. Two main results were observed. First, reaction times were significantly shorter for IOIs of 50, 33 and 25 ms than for an IOI of 100 ms, with no significant differences between IOIs of 33 and 25 ms, highlighting a threshold at 33 ms. Second, average reaction times were significantly shorter for animal sounds than for IOI sounds. Differences in terms of spectral content and temporal modulation characteristics might explain part of these effects. These results also suggest a fast detection advantage for natural animal sounds.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".