Analysis/synthesis of sonar echoes as impact sounds.
Bibliographic record
Abstract
Active sonar performance is sometimes limited by clutter that generates an unacceptable false alarm rate (FAR). High FAR is overcome through the use of signal classification, which is treated here using a sequence of techniques that mimic human perception. The techniques are applied to a corpus of signals that were measured during the experiment Clutter 09, which took place on the Malta Plateau in the spring of 2009. First, techniques for foreground/background separation are presented using whitening and thresholding in a time-frequency representation adapted from computation techniques from acoustic scene analysis. The effects of thresholding are demonstrated with a few signals from the corpus. Modifications, suitable for the noisy sonar-echoes in the corpus, of the natural sound paradigm of Aramaki is presented [Aramaki, et al., Comp. Mus. Mod. Retr. CMMR 2009 (2009)]. Preliminary results of this representation are presented aurally. [Research funded by the Office of Naval Research.]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".