Relative impact of signal-to-noise ratio and propagation effects on the performance of an aural classifier
Bibliographic record
Abstract
Passive acoustic monitoring (PAM) is used to study marine mammals in their habitats, which cover diverse underwater environments. The distinct propagation characteristics of different ocean environments alters the time-frequency characteristics of a recorded signal. This may affect the accuracy of PAM systems. To develop a PAM system capable of operating under numerous environmental conditions, one must account for the impact of propagation. An aural classifier developed at Defence R&D Canada (DRDC) has successfully been used for inter-species discrimination of cetaceans. The aural classifier achieves accurate results by using perceptual signal features that model the features employed by the human auditory system. The current work examines the relative impacts of signal-to-noise ratio (SNR) and propagation effects on the performance of the aural classifier. DRDC’s pulse propagation model, Waveform Transmission Through a Channel (WATTCH), was used to simulate signals travelling through the ocean environment over ranges of 0–20 km. Noise was added to both these signals and the original signals, so that performance could be compared for three scenarios expected to decrease classifier performance: decreasing SNR, increasing propagation effects (frequency spreading, multipath, etc.), and combined SNR and propagation effects. In this presentation, the modeled results are compared to experimental data.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".