Underwater Sensing of Ship-Radiated Noise Based on Interpretable Deep Learning and Acoustic Feature Fusion
Bibliographic record
Abstract
The detection of ship radiated noise holds great significance for the monitoring of marine environment. Deep learning (DL) methods have proven effective in enhancing the efficiency and accuracy of ship-radiated noise classification and have seen widespread application. However, challenges arise due to limited data availability and the difficulty of hydrophone placement for underwater noise signal collection. Additionally, comparative studies across methods remain scarce. This study addresses these challenges by expanding the dataset in two terms of ship model diversity and signal duration. Data fusion with a channel attention module is conducted on three types of acoustic features, which are combined as inputs to the classification models. The classification accuracy of various models and feature combinations is systematically compared. To elucidate the decision strategy for model classification, the Local Interpretable Model-Agnostic Explanation (LIME) algorithm is employed to visualize the distribution of effective classification areas. By integrating acoustic features, our method improves classification accuracy meanwhile provides insights into the underlying reasons for this improvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".