CORVALLIS, Ore. (KOIN) — Researchers from Oregon State University have developed a method to accurately identify calls from a family of fishes.
According to the university, the method takes advantage of data collected by underwater microphones — known as hydrophones — and provides an “efficient and inexpensive way” to understand changes in the marine environment due to climate change and other human-caused influences.
This new research was led by Jill Munger, who works in a lab at OSU, when she was an undergraduate student. Munger came to Oregon State having worked more than 20 years in the corporate world.
“We built a machine learning model on a relatively small set of training data and then applied it to an enormous set of data,” Munger said. “The implications for monitoring the environment are huge.”
As an avid scuba diver, she wanted to study the ocean. Munger received a fellowship from CIMERS to research underwater acoustics with Joe Haxel, who at the time was at the Hatfield Marine Science Center in Newport working with National Oceanic and Atmospheric Administration’s Pacific Marine Environmental Lab’s acoustic program.
Haxel handed her a hard drive with 18,000 hours of acoustic data collected over 39 months in a tropical reef region within the National Park of American Samoa, said the university.
University officials added that the data was collected via a 12-station hydrophone area maintained by NOAA and the National Park Service that is distributed throughout the world in water controlled by the United States.
“(Hydrophones) offer advantages over other types of monitoring because they work at night, in low-visibility conditions and over long periods of time. But techniques to efficiently analyze data from hydrophones are not well developed,” added OSU.
Munger decided to focus on calls from damselfish, in part, because they are distinctive. University said that the fish grind their teeth to create pops, clicks and chirps associated with aggressive behavior and nest defense.
She compared the sound to purring kittens.
“Quickly, it became apparent to her that manually listening to the recordings was not going to work,” said the announcement.
Munger told the university that it was a “slow and tedious process” because she was looking at tiny portion of the data and listening to the rest.
A conversation with her brother, Daniel Herrera, a machine learning engineer, sparked an idea to use machine learning to automate the analysis of the data.
The university told KOIN 6 News that machine learning algorithms build a model based on sample data, known as training data, to make predictions or decisions without being explicitly programmed to do so.
“Machine learning techniques have been used to automate processing of large amounts of data from passive acoustic monitoring devices that collected sound data from birds, bats and marine mammals,” added the university.
In this case, the machine learning sample or training data was 400 to 500 damselfish calls Munger identified by manually listening to the hydrophone recordings. With that start, Herrera, a co-author of the paper, built a machine learning model that accurately identified 94% of damselfish calls, said OSU.
Munger told university officials that she believes machine learning will increasingly be used by scientists to monitor many species of fish in the ocean because it requires relatively little effort.
“The benefit to observing fish calls over a long period of time is that we can start to understand how it’s related to changing ocean conditions, which influence our nation’s living marine resources,” Munger said. “For example, damselfish call abundance can be an indicator of coral reef health.”