Operational Improvements In The Use Of Towed Passive Acoustic Monitoring Of Marine Mammals at Sea During Seismic Surveys.

2012 ◽  
Author(s):  
David Hedgeland ◽  
Chris Pierpoint ◽  
Roy Wyatt ◽  
Cato Rypdal
Keyword(s):  
Marine Mammals ◽  
Acoustic Monitoring ◽  
Passive Acoustic Monitoring ◽  
Seismic Surveys ◽  
Passive Acoustic

Passive acoustic monitoring for marine mammals during Navy explosives training events off the coast of Virginia Beach, Virginia

2013 ◽  
Vol 134 (5) ◽  
pp. 4045-4045
Author(s):  
Cara F. Hotchkin ◽  
Mandy Shoemaker ◽  
Anurag Kumar ◽  
Carl Hager ◽  
David MacDuffee ◽  
...  
Keyword(s):  
Marine Mammals ◽  
Acoustic Monitoring ◽  
Passive Acoustic Monitoring ◽  
Virginia Beach ◽  
Passive Acoustic

scholarly journals On the Importance of Passive Acoustic Monitoring Filters

2021 ◽  
Vol 9 (7) ◽  
pp. 685
Author(s):  
Rafael Aguiar ◽  
Gianluca Maguolo ◽  
Loris Nanni ◽  
Yandre Costa ◽  
Carlos Silla
Keyword(s):  
Marine Mammals ◽  
Statistical Tests ◽  
Acoustic Monitoring ◽  
Passive Acoustic Monitoring ◽  
Species Classification ◽  
Noninvasive Technique ◽  
Significant Difference ◽  
Animal Vocalizations ◽  
Experimental Protocols ◽  
Passive Acoustic

Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example to identify species based on audio recordings. However, some care should be taken to evaluate the capability of a system. We defined PAM filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise patterns, and recording devices in both the training and test sets. It is important to remark that the filters proposed here were not intended to improve the accuracy rates. Indeed, these filters tended to make it harder to obtain better rates, but at the same time, they tended to provide more reliable results. In our experiments, a random division of a database presented accuracies much higher than accuracies obtained with protocols generated with PAM filters, which indicates that the classification system learned other components presented in the audio. Although we used the animal vocalizations, in our method, we converted the audio into spectrogram images, and after that, we described the images using the texture. These are well-known techniques for audio classification, and they have already been used for species classification. Furthermore, we performed statistical tests to demonstrate the significant difference between the accuracies generated with and without PAM filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

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