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Science Quickly

Computers Turn an Ear on New York City

Science Quickly

Scientific American

Science

4.2639 Ratings

🗓️ 10 April 2019

⏱️ 3 minutes

🧾️ Download transcript

Summary

NYU’s “Sounds of New York City” project listens to the city—and then, with the help of citizen scientists, teaches machines to decode the soundscape. Jim Daley reports.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Transcript

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0:00.0

Understanding the human body is a team effort. That's where the Yachtel group comes in.

0:05.8

Researchers at Yachtolt have been delving into the secrets of probiotics for 90 years.

0:11.0

Yacold also partners with nature portfolio to advance gut microbiome science through the global grants for gut health, an investigator-led research program.

0:19.6

To learn more about Yachtolt, visit yawcult.co.

0:22.7

.jp. That's Y-A-K-U-L-T.C-O.J-P. When it comes to a guide for your gut, count on Yacult.

0:33.9

This is Scientific Americans' 60 Second Science.

0:37.8

I'm Jim David.

0:42.9

No wonder they call New York the city that never sleeps.

0:46.2

In fact, noise is one of the biggest civic complaints made by denizens of the Big Apple.

0:51.5

Now, a project that uses citizen science and artificial intelligence,

0:56.0

AI, is trying to help. Called Sounds of New York City, or Sonic, the effort combines a network

1:02.0

of sensors that constantly monitor ambient noise, along with machine learning and human

1:08.0

volunteers.

1:09.0

The Sonic project has two main goals. We want to advance the

1:13.1

science and engineering of machine listening, and we want to help monitor and mitigate

1:17.9

noise pollution in urban areas. Oded Nov, a professor of technology management and

1:23.6

innovation at NYU's Tandon School of Engineering. Over the past two years, our sensors

1:29.1

collected huge amounts of urban sound data, and we need volunteers to label these sounds.

1:35.4

That's where citizen science comes in. Sonic needs members of the public to listen to ambient

1:40.3

sounds picked up by noise monitors and label the sounds so the computers can learn to

1:44.9

independently recognize them. Labeling sound is harder than labeling images because sound is invisible

1:51.2

and ephemeral. But once people label sounds and enter them into a computer, the machines have an

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