Vocalization Source Level Distributions and Pulse Compression Gains of Diverse Baleen Whale Species in the Gulf of Maine

Wang, Delin; Huang, Weiping; Garcia, Heriberto A.; Ratilal, Purnima

doi:10.3390/rs8110881

Cited by 24 publications

(23 citation statements)

References 75 publications

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“…Acoustic recordings exist from the North Atlantic, off the US east coast [54][55][56] and Nova Scotia, Canada [11,12], off the Azores [57], from Hawaii [58], south of New Zealand [59] and Antarctica [60]. There are no recordings of sei whales in Australian waters with simultaneous visual species identification.…”

Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

“…The most frequently reported sounds are simple FM sounds: upsweeps and downsweeps, lasting 0.7-2.2 s, with the fundamental covering a low-frequency band Hz; [54][55][56][57][58][59]) or mid-frequency band (200-600 Hz; [60]). These FM sounds have been recorded with and without harmonics.…”

Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

“…These FM sounds have been recorded with and without harmonics. Source levels of FM and CW sounds are 147-183 dB re 1 µPa rms @ 1 m [ [55][56][57]60]. Very brief (0.03-0.04 s) high-frequency (1.5-3.5 kHz) downsweeps occurring in bouts of 10-20 have been reported from Nova Scotia [11] and also been described as a series of pulses with peak energy at 3 kHz and 7-10 pulses over 0.7 s [12].…”

Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

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Review of Underwater and In-Air Sounds Emitted by Australian and Antarctic Marine Mammals

et al. 2017

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The study of marine soundscapes is a growing field of research. Recording hardware is becoming more accessible; there are a number of off-the-shelf autonomous recorders that can be deployed for months at a time; software analysis tools exist as shareware; raw or preprocessed recordings are freely and publicly available. However, what is missing are catalogues of commonly recorded sounds. Sounds related to geophysical events (e.g. earthquakes) and weather (e.g. wind and precipitation), to human activities (e.g. ships) and to marine animals (e.g. crustaceans, fish and marine mammals) commonly occur. Marine mammals are distributed throughout Australia's oceans and significantly contribute to the underwater soundscape. However, due to a lack of concurrent visual and passive acoustic observations, it is often not known which species produces which sounds. To aid in the analysis of Australian and Antarctic marine soundscape recordings, a literature review of the sounds made by marine mammals was undertaken. Frequency, duration and source level measurements are summarised and tabulated. In addition to the literature review, new marine mammal data are presented and include recordings from Australia of Omura's whales (Balaenoptera omurai), dwarf sperm whales (Kogia sima), common dolphins (Delphinus delphis), short-finned pilot whales (Globicephala macrorhynchus), long-finned pilot whales (G. melas), Fraser's dolphins (Lagenodelphis hosei), false killer whales (Pseudorca crassidens), striped dolphins (Stenella coeruleoalba) and spinner dolphins (S. longirostris), as well as the whistles and burst-pulse sounds of Australian pygmy killer whales (Feresa attenuata). To date, this is the most comprehensive acoustic summary for marine mammal species in Australian waters.

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Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

Section: Balaenoptera Borealis-sei Whalementioning

confidence: 99%

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Review of Underwater and In-Air Sounds Emitted by Australian and Antarctic Marine Mammals

et al. 2017

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“…Each beam-time series was converted to a beamformed spectrogram by short-time Fourier transform (sampling frequency = 8000 Hz, frame = 2048 samples, overlap = 3/4, Hann window). Significant sounds present in the beamformed spectrograms were automatically detected by first applying a pixel intensity threshold detector [45] followed by pixel clustering, and verified by visual inspection [2,8,9,12]. Beamformed spectrogram pixels with local intensity values that are 5.6 dB above the background are grouped using a clustering algorithm according to a nearest-neighbour criteria that determines if the pixels can be grouped into one or more significant sound signals.…”

Section: Fin Whale Vocalization Detection and Identificationmentioning

confidence: 99%

“…A large-aperture densely-populated coherent hydrophone array system typically detects hundreds of thousands to millions of acoustic signals in the 10 Hz to 4000 Hz frequency range for each day of observation in a continental shelf ocean via the passive ocean acoustic waveguide remote sensing (POAWRS) technique [1,2]. The acoustic signal detections include both broadband transient and narrowband tonal signals from a wide range of natural and man-made sound sources [3][4][5][6][7], such as marine mammal vocalizations [1,2,[8][9][10][11], fish grunts, ship radiated sound [12,13], and seismic airgun signals [14]. Here, we focus our efforts on developing automatic classifers for fin whale vocalizations detected in the Norwegian and Barents Seas during our Norwegian Sea 2014 Experiment (NorEx14) [1].…”

Section: Introductionmentioning

confidence: 99%

Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array

et al. 2020

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A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.

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Baleen Whale Vocalizations

White,

Todd

2023

Noisy Oceans

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Vocalization Source Level Distributions and Pulse Compression Gains of Diverse Baleen Whale Species in the Gulf of Maine

Cited by 24 publications

References 75 publications

Review of Underwater and In-Air Sounds Emitted by Australian and Antarctic Marine Mammals

Review of Underwater and In-Air Sounds Emitted by Australian and Antarctic Marine Mammals

Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array

Baleen Whale Vocalizations

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