Automatic classification of impulsive-source active sonar echoes using perceptual signal features from musical acoustics
Bibliographic record
Abstract
The possibility of using human auditory systems as signal features in an automatic classification of impulsive-source active sonar echoes recorded on a towed-array is discussed. It can be demonstrated that active sonar echoes can be successfully classified using perceptual signal features. The perceptual signal features include duration, sub-band attack and decay time, sub-band synchronicity, spectral character of the pre-attack noise, peak value, and the loudness spectrum. The data were collected during a sea trial on the Malta Plateau using signals underwater sound (SUS) charges and a towed array. The towed array data were beamformed to obtain a total of 81 horizontal beams, each of which were spectrally whitened by using a Butterworth filter, and normalized to eliminate reverberation. Results demonstrate that perceptual features with a Gaussian classifier can be used to successfully classify impulsive-source active sonar echoes, and can achieve an error rate less than 10%.
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 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) | 0.003 | 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".