Difference between revisions of "Using AI to save endangered whales"
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== The Solution == | == The Solution == | ||
| − | + | 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. | |
== References == | == References == | ||
Revision as of 22:03, 16 December 2021
Computers can learn to recognize the sound of endangered whales. DFO has developed a predictive model to identify North Atlantic Right Whales from underwater acoustic data. The insights gained from the model can be used to develop a warning system for preventing vessels from fatally striking the endangered species.
The Challenge
The North Atlantic Right Whale (NARW) is one of the most endangered whale species, with only about 366 remaining in the world. In 2017, 12 individuals died in the Gulf of St. Lawrence. The high mortality rate is mainly due to the collision with vessels and the entanglement with fishing gears.
Protection measures, which include vessel speed reduction, fishing closure, and investing in new acoustic technologies, were then put in place by the Department of Fisheries and Oceans (DFO) to prevent the recurrence of such events.
Passive Acoustic Monitoring (PAM) is an observation method where hydrophones are deployed in the ocean to capture sounds from the surrounding environment. Marine mammal acoustic experts periodically, 2 to 4 times a year, collect the recordings from the hydrophones. PAM has become a crucial tool in observing endangered whales.
Currently, acoustic data analysis is performed manually by marine mammal acoustic experts. However, manual recognition is tricky, resource-intensive, time-consuming, and very few scientists can perform it on the fly.
The Solution
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'' 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.
