New Issue: Orbital Catastrophe Ahead? Read Now

Computers Learn to Use Sound to Find Ships

Researchers trained machine-learning algorithms to pinpoint the location of a cargo ship simply by eavesdropping on the sound of its passing. Christopher Intagliata reports.

Illustration of a Bohr atom model spinning around the words Science Quickly with various science and medicine related icons around the text

Join Our Community of Science Lovers!

In The Hunt for Red October, the Soviet submarine captain played by Sean Connery commands his crew to verify the location of a target. <>

That ping is known as "active sonar." Bob Headrick of the Office of Naval Research, the ONR, says it's the audio equivalent of switching on a flashlight. You're getting information, but also broadcasting your location to other ships.

"And you know the number one priority in the submarine is to remain undetected." Subs can keep their secrecy by eavesdropping on other ships instead… listening for propellers and electronics and so on. Such methods, known as "passive sonar,” generally require a skilled operator. But researchers are teaching machines to do it, too.


On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.


They first recorded the underwater rumblings of cargo ships off the California coast <> using an array of 28 underwater microphones. They fed that sound, along with the ships' actual GPS coordinates, to their machine learning algorithms. And then they gave the algorithms new recordings, and asked: where's the ship?

"And it did extremely well." Emma Ozanich, a PhD Student in underwater acoustics at the Scripps Institution of Oceanography. Using the audio data, she says the algorithms pinpointed the ships to within a couple hundred meters, at distances of up to 10 kilometers.

But it's not so clear what the machines now know. "One of the interesting parts about machine learning, especially neural networks, is that it's more difficult to pull out what it's actually learning specifically. It's a little bit of a black box." The research is in The Journal of the Acoustical Society of America. [Haiqiang Niu et al., Ship localization in Santa Barbara Channel using machine learning classifiers]

Bob Headrick of ONR says the data set used here is relatively simple, compared to the real-world scenarios subs would have to solve. Still, he says, with lots more development: "You could conceive with enough effort you create the computer program that can beat the trained operator."

There is a precedent, after all, for machines defeating our best human operators. It was in that other great battle of the cold war: the game of chess.

—Christopher Intagliata

[The above text is a transcript of this podcast.]

Subscribe to Support Independent Journalism

Great science journalism requires human expertise, time, effort and creativity. And it costs money. That’s why I and the journalists here at Scientific American hope you’ll join our community.

When you subscribe, you are supporting staff and freelance journalists who are passionate about telling science stories that are true, important and compelling. Our editors and reporters are often experts in their fields, which means they understand the nuances of big discoveries and can untangle the breakthroughs from the hype. With a subscription, you are also supporting rigorous fact-checking to ensure the words we publish are precise and accurate. And you’re supporting original illustrations, graphics and photos that bring you closer to an advanced laboratory, an ice sheet in Antarctica or a space mission in orbit. You’re helping us craft other types of high-quality journalism as well: Our newsletters are carefully written, edited and curated by staffers you have or will come to know and love. Our Science Quickly podcast is based on original reporting, collaboration with editors and scientists and exacting production.

Subscriptions keep this engine running so we can continue to deliver thoughtful, rigorous and independent science journalism to you. In an era of viral misinformation, this work is crucial. If you value what we do, I hope you’ll consider joining us as a subscriber

Thank you,

Jeanna Bryner, Editor in Chief, Scientific American

Subscribe