Testing the temporal robustness of an automatic aural classifier.
Bibliographic record
Abstract
Military sonar systems must detect and classify submarine threats at ranges safely outside their circle of attack. However, in littoral environments, echoes from geological features (clutter) are frequently mistaken for targets of interest, resulting in degraded performance. Perceptual signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby improving sonar performance. [J. Acoust. Soc. Am. 122, 1502–1517 (2007)] The present work examines the temporal robustness of the aural classifier using data from two field trials: the first in 2007 and the second in 2009. The experiments were conducted on the Malta Plateau using a cardioid towed-array receiver, and a broadband source transmitting linear FM sweeps from 600–3500 Hz. The data set consists of hundreds of pulse-compressed echoes from several surrogate targets and geological clutter objects. The echoes are examined using an automatic classifier that processes each echo to extract perceptual features. Each echo is classified as target or clutter based on the position vector formed by these features. The classifier establishes a boundary between clutter and target echoes in the feature space using the 2007 experiment. Temporal robustness is investigated by testing the classifier on echoes from the 2009 experiment. In this work, the experiments are reviewed and initial results are presented.
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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 | 0.004 | 0.032 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".