A scenario for Right Whale detection in the Bay of Fundy

Desharnais, Francine; Laurinolli, Marjo; Hay, Alex E.; Theriault, James A.

doi:10.1109/oceans.2000.882191

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…23,24 These data were collected using freely-drifting sonobuoys of various types that had been deployed from an aircraft. 24 The data were digitized at a sampling frequency of 6556 Hz.…”

Section: North Atlantic Right Whalesmentioning

confidence: 99%

Automated aural classification used for inter-species discrimination of cetaceans

Binder

Hines

2014

The Journal of the Acoustical Society of America

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Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

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“…23,24 These data were collected using freely-drifting sonobuoys of various types that had been deployed from an aircraft. 24 The data were digitized at a sampling frequency of 6556 Hz.…”

Section: North Atlantic Right Whalesmentioning

confidence: 99%

Automated aural classification used for inter-species discrimination of cetaceans

Binder

Hines

2014

The Journal of the Acoustical Society of America

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“…(Desharnais et al, 2000). It is conceivable that these elevated levels of noise could contribute to hearing loss of some frequencies over long periods of time unless the whales have adaptations for hearing that protect the inner ear.…”

Section: Ifmentioning

confidence: 99%

Acoustic communication in the North Atlantic right whale (Eubalaena glacialis)

Parks¹

2003

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“…A common technique for localizing marine mammals is that of hyperbolic fixing. 4,5,[12][13][14][15][16][17] The measured difference in arrival time of a whale call recorded on multiple hydrophone pairs produces intersecting hyperbolas indicating the animal's position. When the hydrophone pairs are very closely spaced such as on a short towed array or vertical line array ͑VLA͒, hyperbolic fixing is no longer practical.…”

Section: Introductionmentioning

confidence: 99%

Localization of marine mammals near Hawaii using an acoustic propagation model

Tiemann

Porter

Frazer

2004

The Journal of the Acoustical Society of America

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Humpback whale songs were recorded on six widely spaced receivers of the Pacific Missile Range Facility (PMRF) hydrophone network near Hawaii during March of 2001. These recordings were used to test a new approach to localizing the whales that exploits the time-difference of arrival (time lag) of their calls as measured between receiver pairs in the PMRF network. The usual technique for estimating source position uses the intersection of hyperbolic curves of constant time lag, but a drawback of this approach is its assumption of a constant wave speed and straight-line propagation to associate acoustic travel time with range. In contrast to hyperbolic fixing, the algorithm described here uses an acoustic propagation model to account for waveguide and multipath effects when estimating travel time from hypothesized source positions. A comparison between predicted and measured time lags forms an ambiguity surface, or visual representation of the most probable whale position in a horizontal plane around the array. This is an important benefit because it allows for automated peak extraction to provide a location estimate. Examples of whale localizations using real and simulated data in algorithms of increasing complexity are provided.

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A scenario for Right Whale detection in the Bay of Fundy

Cited by 6 publications

References 7 publications

Automated aural classification used for inter-species discrimination of cetaceans

Automated aural classification used for inter-species discrimination of cetaceans

Acoustic communication in the North Atlantic right whale (Eubalaena glacialis)

Localization of marine mammals near Hawaii using an acoustic propagation model

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