Blind channel estimation of underwater acoustic waveguide impulse responses using marine mammal vocalizations
Bibliographic record
Abstract
Some techniques for underwater passive acoustic localization make use of estimates for the direct and/or interface-reflected acoustic arrival times of an acoustic source at one or more underwater hydrophones. This estimation task can be difficult for non-impulsive sound sources, such as humpback whales, due to later arrivals being masked by earlier ones. In linear system analysis, the impulse response of a system is the system output when the input is an impulse (i.e., a short duration, large bandwidth signal). The impulse response of a system contains information about the propagation environment, and expresses how a source signal interacts with the environment to yield the output waveform (e.g., that recorded by a hydrophone). The recording of a relatively impulsive call (e.g., a sperm whale click) approximates the impulse response between the whale and hydrophone. In recordings of such a call, arrival time estimation can be straightforward. For non-impulsive calls, if the source waveform is known, the impulse response can be estimated through techniques such as deconvolution or cross-correlation. The case of unknown, non-impulsive calls is particularly challenging. However, given the wealth of information present in the impulse response, we seek to estimate impulse responses even in these adverse conditions. Blind channel estimation (BCE) is the process of estimating the set of impulse responses between a single (frequently, unknown) source and multiple receivers, and can potentially help estimate direct and interface-reflected arrival times for non-impulsive marine mammal vocalizations since the impulse responses can be analyzed rather than the waveforms or spectra. In this paper, we look at simulation and measured-data results using marine mammal vocalizations to estimate underwater acoustic impulse responses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".