MétaCan
Menu
Back to cohort
Record W2017069111 · doi:10.1109/oceans.2014.7003266

Passive energy based acoustic signal analysis for diver detection

2014· article· en· W2017069111 on OpenAlexaffabout
Hannan Lohrasbipeydeh, Tom Dakin, T. Aaron Gulliver, Claire de Grasse

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed Sensor Networks and Detection Algorithms
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEnergy (signal processing)SIGNAL (programming language)Spectral densityDetectorAcousticsComputer scienceDetection theorySignal processingTime–frequency analysisBioacousticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A passive energy based analysis of the acoustic signal from a diver is presented. The frequency components of the signal during inhaling and exhaling are investigated, as well as the periodicity of the signal features. The effect of different regulators on the power spectral density of the signals is evaluated. The energy based detection problem is formulated using the time-frequency Teager Energy Operator (TEO) as it can be used to develop an adaptive detector. To evaluate the proposed approach, experimental data is employed which was recorded in the open ocean at different ranges and depths. This data was obtained using accurate hydrophones provided by Ocean Network Canada (ONC).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.201
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2014
Admission routes2
Has abstractyes

Explore more

Same topicDistributed Sensor Networks and Detection AlgorithmsFrench-language works237,207