Application of Tonal Tracking to Ship Acoustic Signature Feature Identification
Bibliographic record
Abstract
A ship’s radiated acoustic signature is known after a range measurement, but it changes from that the longer the ship is in-service. The Ship Signatures Management System (SSMS) provides an organic, real-time capability for a naval ship to monitor its own signature in order to evaluate the impact of proposed actions on its counter-detection range and sensor performance. Ship protection is enhanced through insightful and timely signature data. In particular, this paper discusses the tonal detection and tracking algorithms used to monitor on-board machinery and propeller activity. The paper specifically addresses tonals that appear or disappear as a consequence of changes in the background level, as well as that of crossed tonals. This is of significance because it impacts the SSMS’s ability to attribute cause to changes in the ship acoustic signature. In particular, it is impossible to associate tonals that are time synchronized in their frequency and intensity changes as being created by a single cause (e.g., piece of machinery) with a known tonal set. The use of tonal amplitude and the cause for the signal-to-noise ratio change, in addition to the signal-to-noise ratio, remedies the detection and tracking of tonals that appear/disappear relative to the background. The additional use of tonal width is suggested as a means to remedy the problem of crossed tonals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".