Signal detection as a function of relative acoustic entropy.
Bibliographic record
Abstract
Natural sounds are far from random. Instead, they possess structure that reflects physical constraints on sound-producing objects and events. Listeners likely exploit such structure (predictability and redundancy) when perceiving complex natural sounds. The present experiments investigate listeners’ ability to discover and use structure in novel sounds to identify a repeating target sound against a competing background of sounds. Complementary signal-processing strategies independently varied acoustic entropy (complement to redundancy) in frequency or time. Novel sounds were generated by stretching the entropy in a restricted frequency band (instantaneous frequency dilation) or temporal slice (phase vocoding) of pink (1/f) noise across the bandwidth or duration, respectively, of an unstretched noise sample. Listeners matched a probe sound to a target sound (1%, 3.2%, or 10% entropy of pink noise) that repeats amidst distractor sounds (1%, 10%, or 100% entropy) at 0-dB SNR. In separate experiments varying entropy in frequency or time, identification of target sounds depended critically on the difference in entropy across targets and distractors. Listeners were more proficient identifying lower-entropy targets against higher-entropy distractors but not lower-entropy distractors. Similarly, higher-entropy targets were better identified amidst lower-entropy distractors. Potential implications for cochlear implant processing will be discussed. [Work supported by NIDCD.]
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".