A real-time underwater soundscape classification system
Bibliographic record
Abstract
The study of underwater soundscapes, pioneered by Dr. Jeff Nystuen, includes information about geophonic, biological, and anthropogenic processes, many of which have distinct spectral characteristics. The contributors to the underwater ambient sound field can be quantified and classified using knowledge of these spectra. Long-term acoustic data recordings from a wide variety of depths and locations with high sampling frequencies have been analyzed to develop a robust soundscape classification algorithm based on Dr. Nystuen’s methods. Each one-minute of data has been evaluated to classify the soundscape into wind, rainfall, drizzle, heavy shipping, light shipping, other vessel activity, and biological phenomena. The power spectral density (PSD) level at twelve frequencies in the range of 0.03–30 kHz, as well as the spectral slope for the frequency range between 8 and 15kHz and kurtosis are used for the passive classification algorithm. After classification, the wind speed was quantified as a cubic function of PSD at 6 kHz and recording depth. The wind speed estimated from the acoustics compared very well to satellite data for speeds lower than 15 m/s. The classification algorithms are being embedded on a processor using Xilinx’s Zynq System-on-Chip that produces a 32-kHz hybrid millidecade spectrum in real-time on a logarithmic scale.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".