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Record W2134641446

Feature-Aided Tracking for Marine Mammal Detection and Classification

2008· article· en· W2134641446 on OpenAlexvenueno aff
Odile Gérard, Craig Carthel, Stefano Coraluppi, Peter Willett

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNaval Undersea Warfare CenterOcean Life Institute, Woods Hole Oceanographic InstitutionWoods Hole Oceanographic Institution
KeywordsBeaked whaleHuman echolocationComputer scienceWhaleBioacousticsMarine mammalFeature (linguistics)Artificial intelligenceSperm whaleTracking (education)Pattern recognition (psychology)Speech recognitionAcousticsBiologyEcologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a method to detect and classify odontocete echolocation clicks as well as to estimate the number of animals that are vocalizing.A transient detector using the Page test [1-3] is used to extract the clicks: the click time, the click duration, the click amplitude and the spectral information of the clicks are extracted.A probability distribution over the species is assigned to each click, based on the spectral information of the click.The estimation of the number of animals is done using feature-aided multi hypothesis tracking (MHT) algorithms.The association is based on the assumptions of slowly-varying click amplitude and intra-click timing [4][5].This work has been done on the dataset provided by the organizers of the 3rd International Workshop on the Detection and Classification o f Marine Mammals using Passive Acoustics, Boston, July 2007.This dataset consists of training and test data; the training data includes vocalizations of three species: Blainville's beaked whale (Mesoplodon densirostris), Risso's dolphin (Grampus griseus) and short-finned pilot whale (Globicephala macrorhynchus). s o m m a i r eCet article présente une méthode de détection et classification de clics d 'écholocation d 'odontocètes ainsi que d'estimation du nombre d 'animaux vocalisant en même temps.Un détecteur de transitoires utilisant le test de Page [1-3] permet d 'extraire les clics : leurs instants, durées et amplitudes ainsi que leurs spectres sont stockés.L 'analyse du spectre d 'un clic permet de lui affecter une probabilité de distribution parmi les différentes espèces.L 'estimation du nombre d 'animaux se fait à l'aide d 'un algorithme de tracking (multi hypothesis tracking MHT).L 'association des clics est basée sur l'hypothèse que l 'amplitude et l'intervalle entre deux clics varient lentement en fonction du temps.Ce travail a été réalisé sur le jeu de données mis à disposition par les organisateurs du 3rd International Workshop on the Detection and Classification o f Marine Mammals using Passive Acoustics, Boston, Juillet 2007.Ce dernier se compose de données d 'entrainement sur trois espèces : Mésoplodon de Blainville (Mesoplodon densirostris), dauphins de Risso (Grampus griseus) et globicéphales (Globicephala macrorhynchus) et de fichiers test.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.031
GPT teacher head0.223
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

Citations11
Published2008
Admission routes1
Has abstractyes

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