Sparsity-based shipping noise analysis and cancellation in underwater acoustic communication
Bibliographic record
Abstract
We are interested in achieving reliable underwater acoustic communication (UWAC) in the presence of noise generated by vessel traffic, i.e., shipping noise. Our general approach is to determine an underlying structure in shipping noise and to exploit this structure to remove it from desired UWAC signals. Since a well-established statistical characterization of shipping noise is not available, in this paper, we discuss our evaluation of shipping noise data sets obtained from labeled recordings from an underwater acoustic hydrophone array deployed in the Strait of Georgia by Ocean Networks Canada. Motivated by the general observation that shipping noise exhibits a structure akin to impulse noise, we study to what extent shipping noise can be described as a sparse signal. Specifically, we apply the expectation-maximization method to train a Gaussian-mixture model with two components, in which the large variance component represents the impulse-noise part. In a second step, we use the model to perform noise cancellation in a multicarrier UWAC scenario. For this purpose, we apply a sparse-signal recovery formulation and a message passing algorithm for detection, i.e., a form of compressed sensing. Numerical results show the moderate level of mitigation of shipping noise interference achieved with the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".