Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio

Gervaise, Cédric; Barazzutti, A.; Busson, Sylvain; Simard, Yvan; Roy, Nathalie

doi:10.1016/j.apacoust.2010.05.009

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…Pour séparer les trains, la fonction ASH utilise les temps d'arrivées de clics. Ces temps d'arrivées peuvent être obtenus en utilisant un détecteur de clic standard (Gervaise et al, 2010 ;Kandia, Stylianou, 2006).…”

Section: Présentation Des Donnéesunclassified

“…Seulement, face à la quantité importante de données enregistrées par ces systèmes, une telle méthode prend beaucoup de temps et devient par conséquent fastidieuse. Afin d'automatiser ces analyses, la communauté bioacoustique a développé des outils de traitement du signal pour détecter les clics d'odontocètes, par exemple (Kandia, Stylianou, 2006 ;Gervaise et al, 2010). Le suivi des trains de clics a été majoritairement étudié à des fins de localisation des cachalots lors de leurs plongées (Laplanche et al, 2005 ;Giraudet, Glotin, 2006 ;Caudal, Glotin, 2008a ;Morrissey et al, 2006 ;Caudal, Glotin, 2008b ;Nosal, Frazer, 2007), mais aussi des baleines à bec (Ward et al, 2008) et plus récemment des belugas (Roy et al, 2010).…”

Section: Introductionunclassified

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Désentrelacement de clics par analyse du rythme

Bot¹,

Gervaise²,

Bonnel³

et al. 2013

Traitement du signal

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Tous les odontocètes émettent de courtes impulsions acoustiques (clics) pour satisfaire une activité de localisation, de chasse ou de communication. Ces impulsions sont le plus souvent émises en trains rythmés. Il est très fréquent d'enregistrer simultanément les clics de plusieurs animaux. Cet article présente une méthode permettant de séparer les trains de clics enregistrés sur un unique hydrophone afin de connaître le nombre d'animaux ayant émis simultanément. La méthode proposée est testée avec succès sur des données simulées et sur des données réelles. ABSTRACT. All odontocetes produce short acoustic pulses called clicks to perform localization, hunt preys or communicate. These clicks are generally emitted in trains having a certain rhythm. Frequently, click trains from several animals are recorded simultaneously. This paper introduces a method for deinterleaving these click trains with a single hydrophone. As final results it gives the number of animals clicking simultaneously and allows the tracking of rhythm variations within each train. The proposed algorithm is successfully tested on simulated data and validated on real data.

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Section: Présentation Des Donnéesunclassified

Section: Introductionunclassified

Désentrelacement de clics par analyse du rythme

Bot¹,

Gervaise²,

Bonnel³

et al. 2013

Traitement du signal

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“…The success of Kurtosis in vibration signals is based on the fact that vibration signals of a system under stress or having defects differs from those of a normal system (Antoni, 2006;Li and Wu, 1989). The Kurtosis has the ','sym2','sym3','sym4','sym5','sym6','sym7','db1','db2','db3','db4','db5','db6',' advantage of being independent of changes in the speed of rotation or loads of the machine (Carrato and Ramponi, 1993;Gervaise et al, 2010). The relevance of optimizing the denoising process should be noted.…”

Section: Selection Of Parameters Of the Wavelet Decompositionmentioning

confidence: 99%

Performance of wavelet denoising in vibration analysis: highlighting

Chiementin

Kilundu

Rasolofondraibe

et al. 2011

Journal of Vibration and Control

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This paper proposes to highlight two aspects of denoising in vibration analysis. The first aspect aims to reveal the singularities, and the second eliminates the noise in order to keep the useful signal. These two aspects are the cause of the surjection of denoising, especially due to the choice of the performance criteria. This paper highlights the use of denoising through these aspects, and then proposes a performance criterion suitable for vibration analysis as part of a noise suppression, to apply a processing method. This paper provides a reflection on the use of discrete wavelet transform through the various parameters which are used during processing.

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“…For the opposite situation, when the waveform of the deterministic signal is not known, classical detectors are usually based on signal energy [1] or on high-order statistics [2,3], and perform non-Gaussianity tests. Also, there are several approaches that can be used to set the detection threshold, including the Neyman-Pearson method, the Bayes' criterion, the maximum a posteriori, and the false discovery rate [1].…”

Section: Introductionmentioning

confidence: 99%

Similarity matrix analysis and divergence measures for statistical detection of unknown deterministic signals hidden in additive noise

2015

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International audienceThis Letter proposes an algorithm to detect an unknown deterministic signal hidden in additive white Gaussian noise. The detector is based on recurrence analysis. It compares the distribution of the similarity matrix coefficients of the measured signal with an analytic expression of the distribution expected in the noise-only case. This comparison is achieved using divergence measures. Performance analysis based on the receiver operating characteristics shows that the proposed detector outperforms the energy detector, giving a probability of detection 10% to 50% higher, and has a similar performance to that of a sub-optimal filter detector

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Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio

Cited by 14 publications

References 20 publications

Désentrelacement de clics par analyse du rythme

Désentrelacement de clics par analyse du rythme

Performance of wavelet denoising in vibration analysis: highlighting

Similarity matrix analysis and divergence measures for statistical detection of unknown deterministic signals hidden in additive noise

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