Applying automatic aural classification to cetacean vocalizations

Binder, Carolyn M.; Hines, Paul C.

doi:10.1121/1.4770058

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…A supervised learning algorithm then maps these attributes to a call class after learning training examples labeled by human analysts. Classifier of this kind include aural classification [27], neural networks [3], hidden Markov models [28], quadratic discriminant function analysis [29], Gaussian mixture models [30] or classification trees [31]. More recently, Halkias et al [25] proposed an alternative approach based on hybrid generative/discriminative models commonly used in machine learning.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation-based classification of mysticete calls

Guilment

Socheleau

Pastor

et al. 2018

The Journal of the Acoustical Society of America

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This paper presents an automatic classification method dedicated to mysticete calls. This method relies on sparse representations which assume that mysticete calls lie in a linear subspace described by a dictionarybased representation. The classifier accounts for noise by refusing to assign the observed signal to a given class if it is not included into the linear subspace spanned by the dictionaries of mysticete calls. Rejection of noise is achieved without feature learning. In addition, the proposed method is modular in that, call classes can be appended to or removed from the classifier without requiring retraining. The classifier is easy to design since it relies on a few parameters. Experiments on five types of mysticete calls are presented. It includes Antarctic blue whale Z-calls, two types of "Madagascar" pygmy blue whale calls, fin whale 20 Hz calls and North-Pacific blue whale D-calls. On this dataset, containing 2185 calls and 15000 noise samples, an average recall of 96.4% is obtained and 93.3% of the noise data (persistent and transient) are correctly rejected by the classifier.

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Section: Introductionmentioning

confidence: 99%

Sparse representation-based classification of mysticete calls

Guilment

Socheleau

Pastor

et al. 2018

The Journal of the Acoustical Society of America

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“…Previous effort has shown that a prototype aural classifier developed at Defence R&D Canada [1] can be used to successfully discriminate cetacean vocalizations from several species; bowhead, humpback, North Atlantic right, and sperm whales vocalizations have been classified in this way [2]. To achieve these accurate results, the aural classifier employs perceptual signal features, which model the features used by the human auditory system.…”

Section: Introductionmentioning

confidence: 99%

“…Like the human auditory system, the perceptual features used by the aural classifier can be employed to classify sounds from different sources, e.g. active sonar echoes [1] and cetacean vocalizations [2]. Thus, the aural classifier has proven to be very robust in its applicability to different data sources.…”

Section: Introductionmentioning

confidence: 99%

Robustness of perceptual features used for automatic aural classification to propagation effects

Binder

Hines

Pecknold

et al. 2013

The Journal of the Acoustical Society of America

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Previous effort has shown that a prototype aural classifier developed at Defence R&D Canada can be used to reduce false alarm rates and successfully discriminate cetacean vocalizations from several species. The aural classifier achieves accurate results by using perceptual signal features that model the features employed by the human auditory system. Current work focuses on determining the robustness of the perceptual features to propagation effects for two of the cetacean species studied previously-bowhead and humpback whales. To this end, classification results are compared for the original vocalizations to classification results obtained after the vocalizations were re-transmitted underwater over ranges of 2 to 10 km. Additional insight into the propagation effects is gained from transmission of synthetic bowhead and humpback vocalizations, designed to have features similar to the most important aural features for classification of bowhead and humpback vocalizations. Each perceptual feature is examined individually to determine its robustness to propagation effects compared to the other aural features. To gain further understanding of propagation effects on the features, preliminary propagation modelling results are presented in addition to experimental data.

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“…Em geral são usados métodos baseados na correlação (Abbot et al, 2010;Mellinger, 2004;Mellinger et al, 2011;Nanaware et al, 2014), redes neurais (Dugan et al, 2010;Mellinger and Clark, 2000), métodos estatísticos multivariados (Binder and Hines, 2012;Diaz Lopez, 2011), dentre outros. Esses métodos supõem que amostras do padrão do evento acústico de interesse sejam conhecidas e estejam disponíveis.…”

Section: Métodos De Detecçãounclassified

Sistema para monitoramento e análise de paisagens acústicas submarinas.

Rosario¹

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em análise estatística da representação tempo-frequência dos sinais acústicos. Este novo método foi testado na detecção de cetáceos, presentes no banco de dados gerado pelas missões de monitoramento. P alavras − chave: Monitoramento acústico passivo submarino. Detecção de eventos bioacústicos submarinos. Processamento de sinais acústicos submarinos. AbstractPassive acoustic monitoring (PAM) refers to the use of systems to listen and record underwater soundscape, in order to detect, track and identify sound sources through the pressure waves that they produce. It is said to be passive as these systems only hear, not put noise in the environment, such as sonars.Underwater PAM has various application areas, such as military surveillance systems, port security, environmental monitoring, development of population density rates of species, species identification, etc.National technology in the field is practically nonexistent despite its importance. In this context, this paper aims to contribute to the national technology development in the field by designing, building, and operating a self-contained PAM equipment, also developing signal-processing methods for automated detection of underwater acoustic events.A device, named "OceanPod"which has characteristics such as low manufacturing cost, flexibility and ease of setup and use, intended for scientific, industrial research and environmental control was developed. Several prototypes of the equipment were built and used in several missions at seawaters.These missions monitoring, enabled start creating an acoustic database, which provided the raw material for the automated acoustic events detectors and realtime test. Additionally, it is also proposed a new method of detecting, identifying sound events, based on statistical analysis of the time-frequency representation of the acoustic signals. This new method has been tested in the detection of cetaceans present in the database generated by missions monitoring.

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Applying automatic aural classification to cetacean vocalizations

Cited by 7 publications

References 4 publications

Sparse representation-based classification of mysticete calls

Sparse representation-based classification of mysticete calls

Robustness of perceptual features used for automatic aural classification to propagation effects

Sistema para monitoramento e análise de paisagens acústicas submarinas.

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