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Record W1835868473

Detection of clicks based on group delay

2008· article· en· W1835868473 on OpenAlexvenueno aff
Varvara Kandia, Yannis Stylianou

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNaval Undersea Warfare Center
KeywordsRobustness (evolution)Computer scienceSpeech recognitionGroup delay and phase delaySet (abstract data type)Noise (video)Artificial intelligenceFilter (signal processing)Computer vision
DOInot available

Abstract

fetched live from OpenAlex

In this paper we present a novel approach for the automatic detection of clicks from recordings of beaked whales based on the phase characteristics of minimum phase signals and especially using the group delay function.Group delay is estimated through the and first derivative of the Fourier Transform of a signal.A major advantage of the proposed approach is its robustness against additive noise while it doesn't require the definition of ad-hoc or adaptive thresholds for the detection of clicks.This method works on raw recordings which are usually quite noisy as well as on click enhanced recordings (after band-pass filtering or using operators like the Teager-Kaiser energy operator).Moreover, a click is just detected by searching the positive zero crossings over time of the slope of the phase spectrum.To evaluate the effectiveness of the proposed approach in detecting clicks, a oneminute recording has been manually marked providing a test set of about 320 clicks.Results show that the proposed approach was able to detect 71.37% of the hand labelled clicks within an accuracy of 3 ms. SOMMAIREDans cet article, nous prsentons une nouvelle approche pour la dtection automatique de clics sur des enregistrements de baleines bec exploitant les caractristiques de signaux phase minimale notamment via l' utilisation de la fonction de retard de groupe.Le retard de groupe est estim partir de la transforme de Fourier d'un signal et de la drive de celle-ci.L'approche propose est robuste vis--vis du bruit additif et ne requiert pas la dfinition ad-hoc ou adaptative de seuils pour la dtection de clics.Elle permet de traiter aussi bien des enregistrements bruts fortement bruits que des enregistrements rehausss (aprs filtrage passe-bande ou l'aide d'oprateurs tels que l'oprateur d'nergie de Teager-Kaiser).De plus, un clic est simplement dtect en recherchant un passage par zro sur la partie croissante de la pente du spectre de phase.Pour valuer l'efficacit de l'approche propose dtecter des clics, une minute d'enregistrement a t annote manuellement, fournissant ainsi un ensemble de test d'environ 320 clics.Les rsultats montrent que l'approche propose parvient dtecter 71.37% des clics marqus manuellement avec une prcision de 3 ms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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 designObservational
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

Citations7
Published2008
Admission routes1
Has abstractyes

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