The Sounds of New York City (SONYC) is featured by The Verge.
Cities are noisy places, and a team of scientists believe that sensors, artificial intelligence, and some generous volunteers can help solve the problem. Sounds of New York City(SONYC) is asking citizen scientists to listen to 10-second sound clips collected by sensors around the city and identify what they hear.
Users are presented with a spectrogram visualization of the audio and a menu of options (“small-sounding engine,” “dog barking,” “ice cream truck”) and have to select all the options that apply. This information will then be fed to an algorithm that will learn to better identify the sources of noise on its own. Hopefully, all this will lead to a better understanding of noise pollution and better tools for fighting it.
SONYC is a five-year collaboration between the city of New York, New York University, and Ohio State University. The Verge spoke to Mark Cartwright, an NYU postdoc who’s working on the project, to learn more about how labeling sound clips could lead to a quieter city. This interview has been lightly edited for clarity.
The ultimate goal of the SONYC project is to combat noise pollution. What are the negative effects of noise pollution, aside from being merely annoying?
It’s one of the most complicated issues in the city. It creates health effects, sleep disruption, hearing loss, learning impairment, and there are economic effects as well. We would like to make New York a quieter place.
The project as a whole tries to monitor, analyze, and mitigate the noise pollution. First, we deployed a sensor network throughout the city and then we trained our models to identify the different sources of noise in the city, along with the loudness that they’re measuring. From this, we can have a better understanding of the noise and then build tools for city agencies to help them understand the noise better and enforce the noise codes.
Volunteers are asked to identify the type of noise. Why does the type matter if you already know how loud the area is?
It makes things more actionable. If we just knew that there’s a loud noise in a particular part of the city and we didn’t know what caused it, it’s hard to mitigate the noise. But if we know that it’s a pile driver that’s causing this large peak in sound, we could potentially measure it if that was exceeding the thresholds in the noise code, for example. We could deploy inspectors to investigate that further.
We’re working closely with city agencies to help them and build tools for them. That’s more future work, so I can’t say a lot about it now, but we want to help for example, construction workers understand the noise footprint and self-regulate so they don’t get fined and citizens understand noise in the city and make more informed decisions about their life. We also want to make this data available for people to build their own tools on top of, as well.