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Line 23: + + + + + + + + + − The deployed hydrophones can detect whale calls and transmit this information in near real-time, providing continuous round-the-clock information over the season. Automating the process of acoustic data analysis can result in near real-time detection of NARW. The main idea is to "teach<nowiki>''</nowiki> a computer to recognize the sounds of NARW from the acoustic recordings by identifying specific patterns in the data. Eventually, the goal is to make a system that will monitor the sounds from whales' calls from a network of hydrophones every hour to detect whales and send a real-time warning to vessels to slow down or change their course when the whales are present.
Using AI to save endangered whales (view source)
Revision as of 22:43, 16 December 2021
, 22:43, 16 December 2021→The Solution
== The Solution ==
== The Solution ==
[[File:NARW Detector1.png|thumb|258x258px|<small>'''AI-powered near real-time detector of NARW'''</small>]]
The deployed hydrophones can detect whale calls and transmit this information in near real-time, providing continuous round-the-clock information over the season. Automating the process of acoustic data analysis can result in near real-time detection of NARW. The main idea is to "teach<nowiki>''</nowiki> a computer to recognize the sounds of NARW from the acoustic recordings by identifying specific patterns in the data. Such a tool can drastically minimize to time consumed by marine biologists to perform manual acoustic data analysis from 14 days to 4 to 5 hours <ref>https://gcn.com/2020/04/ai-streamlines-acoustic-id-of-beluga-whales/291304/</ref>.
[[File:Narw detector.png|thumb|559x559px|'''<small>Predictive model for detecting NARW upcalls</small>''']]
Supported by the 2020 – 2021 Results Fund, a Proof of Concept (POC) was developed a predictive model for the automated detection of NARW upcalls from acoustic data. The detection problem is formulated as a binary image classification problem where the image is a spectrogram. A spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time and is considered an effective method of displaying marine mammal vocalizations.
The POC was developed using the Open source Ketos toolkit <ref>https://docs.meridian.cs.dal.ca/ketos/</ref>, provided by Dalhousie University, for underwater acoustic analysis. The AI team at DFO has also collaborated with researchers from Dalhousie University to further enhance the accuracy of the predictive model in differentiating between two kinds of whales: Humpback whales and North Atlantic Right whales.
Eventually, the goal is to develop a system that will monitor the sounds from whales' calls from a network of hydrophones every hour to detect whales and '''send a real-time warning to vessels to slow down or change their course when the whales are present.'''
== References ==
== References ==
