Classifying sonar signals with varying signal-to-noise ratio and bandwidth
Bibliographic record
Abstract
An automatic aural classifier developed at Defence Research and Development Canada has demonstrated the ability to distinguish target echoes from clutter using perceptual-based features inspired by sonar operators. Initially, the classifier was tested with echoes from explosive sources, but more recent research involved transmitting broadband waveforms from transducer sources. In sonar transducer operation, there is a trade off between source level and bandwidth, and the goal of this paper is to study how these factors affect echo classification. Source level relates to signal-to-noise ratio (SNR), which inherently affects classification since signals with low enough SNR cannot be distinguished from noise, let alone other signals. The dependence of classification performance on bandwidth is less obvious; however, the aural classification technique is based on a sub-band type of processing that mimics the basilar membrane in the human auditory system, and this model is not well adapted for narrow bands. Performance of the aural classifier is therefore expected to degrade as bandwidth is decreased. In this paper, the effect of SNR and signal bandwidth on echo classification is examined using echoes of varying SNR, and in various bands selected using band-pass filters.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".