Machine learning inspired ship-radiated noise modelling and cancellation for underwater acoustic communication systems
Bibliographic record
Abstract
Achieving high data rate and reliable communication in shallow and harbour underwater acoustic (UA) environments can be a demanding task in the presence of ship-radiated noise. However, few research studies have examined the properties of ship-radiated noise in terms of its time-domain statistical characteristics and its negative effects on UA communication systems. From the observation of spectrograms and the temporal signals of various acoustic shipping noise recordings, high frequency and impulsive characteristics are visible. These impulsive agitations can be detrimental to the performance of multi-carrier UA communication systems, thus impulse noise cancellation methods are necessary to reduce errors. In this thesis, we investigate the impulsive and correlative interference generated due to nearby shipping activity and its effects on orthogonal frequency-division multiplexing (OFDM) systems. The research objectives are twofold: (1) model the time-domain stochastic characteristics of ship-radiated noise, and (2) achieve shipping noise cancellation for UA OFDM systems. We propose the use of unsupervised learning techniques to train generative models that capture the time-domain stochastic behaviours of ship-radiated noise using a publicly available database of long-term acoustic shipping noise recordings. These models can then be used for further analysis of ship-radiated noise and performance evaluation of UA OFDM systems in the presence of such interference. The results indicate a two component Gaussian mixture model serves as a better approximation for high frequency ship-radiated noise while generative adversarial networks produce improved realizations of shipping noise in lower frequencies. We offer sparsity and deep learning-based ship-radiated noise cancellation solutions that are constructed under a compressed sensing framework. Obtained results show that the sparsity-based estimation and cancellation algorithms demonstrate competitive mitigation capabilities for high frequency impulsive ship-radiated noise. The deep learning-based cancellation methods depict measurable shipping noise mitigation results to the sparsity-based techniques, but with superior run-time performance. In addition, the deep learning-based methods outperform the sparsity-based approaches in lower frequency ship-radiated noise due to the supplementary correlative structure. Furthermore, experimental results indicate the deep learning-based cancellation approaches scale better to new realizations of high frequency and low frequency shipping noise signals compared to the sparsity-based methods. [An errata to this thesis/dissertation was made available on 2021-02-18.]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".