Performances of human listeners and an automatic aural classifier in discriminating between sonar target echoes and clutter
Bibliographic record
Abstract
Human listening tests were conducted to investigate if participants could distinguish between samples of target echoes and clutter obtained from a broadband active sonar experiment. For each echo, the listeners assigned a rating based on how confident they were that it was a target echo or clutter. The measure of performance was the area under the binormal receiver-operating-characteristic (ROC) curve, A(z). The mean performance was A(z)=0.95 ± 0.04 when signals were presented with their full available acoustic bandwidth of approximately 0-2 kHz. It was A(z)=0.77 ± 0.08 when the bandwidth was reduced to 0.5-2 kHz. The error bounds are stated as 95% confidence intervals. These results show that the listeners could definitely hear differences, but their performance was significantly degraded when the low-frequency signal information was removed. The performance of an automatic aural classifier was compared against this human-performance baseline. Results of statistical tests showed that it outperformed 2 of 13 listeners and 5 of 9 human listeners in the full-bandwidth and reduced-bandwidth tests, respectively, and performed similarly to the other listeners. Given its performance, the automatic aural classifier may prove beneficial to Navy sonar systems.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".