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Record W3116488671 · doi:10.1121/10.0002922

Quantifying the contribution of ship noise to the underwater sound field

2020· article· en· W3116488671 on OpenAlexafffund
Najeem Shajahan, David R. Barclay, Ying-Tsong Lin

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAmbient noise levelAcousticsNoise (video)Coherence (philosophical gambling strategy)Underwater acousticsSound powerUnderwaterGeologySpectral densityNoise floorEnvironmental scienceNoise measurementComputer sciencePhysicsNoise reductionSound (geography)TelecommunicationsOceanography

Abstract

fetched live from OpenAlex

The ambient sound field in the ocean can be decomposed into a linear combination of two independent fields attributable to wind-generated wave action at the surface and noise radiated by ships. The vertical coherence (the cross-spectrum normalized by the power spectra) and normalized directionality of wind-generated noise in the ocean are stationary in time, do not vary with source strength and spectral characteristics, and depend primarily on the local sound speed and the geoacoustic properties which define the propagation environment. The contribution to the noise coherence due to passing vessels depends on the range between the source and receiver, the propagation environment, and the effective bandwidth of the characteristic source spectrum. Using noise coherence models for both types of the sources, an inversion scheme is developed for the relative and absolute contribution of frequency dependent ship noise to the total sound field. A month-long continuous ambient sound recording collected on a pair of vertically aligned hydrophones near Alvin Canyon at the New England shelf break is decomposed into time-dependent ship noise and wind-driven noise power spectra. The processing technique can be used to quantify the impact of human activity on the sound field above the natural dynamic background noise, or to eliminate ship noise from a passive acoustic monitoring data set.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.290
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207