Can sound give data meaning?

By Zahra Sethna, Head of Content and Communications for Equal Measures 2030

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Riders on the New York City subway in 2013. (Susan Sermoneta/Flickr)

Close your eyes and quickly picture what income inequality looks like. It’s probably safe to assume you could easily conjure up an image — maybe a homeless person sleeping in front of a luxury apartment?

Now picture what about income inequality looks like. It might have taken you a few more seconds, but did you imagine a graph or chart depicting how a small proportion of the population holds a large share of wealth?

The visual narrative around this issue is fairly stark. But what would happen if I asked you to imagine what income inequality data like? Much harder, right?

Brian Foo, a New York City-based artist and programmer, took on that challenge a few years ago.

“As an artist, I’ve never been interested in traditional formats,” he says. He prefers to explore art forms that evoke visceral reactions and are vehicles for story telling.

Although he doesn’t have a musical background, Foo decided that music would be the perfect format for a guided journey through a time-based experience to explore a data story more fully.

“A song is linear,” he explains. “You can intuitively hear contrasts. I wanted to explore income inequality in New York City and make that into a song that gets stuck in your head.”

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A screen grab from Two Trains, by Brian Foo

Foo mapped census data against subway data, selected music samples and then wrote algorithms that automatically generated the song as output. This way, the shape of the data is the only thing that dictates the shape of the song.

‘Two Trains’ takes the listener on a virtual ride on a subway through three of Manhattan’s boroughs. Sounds correlate to the median income of the neighbourhood: When wealth is higher, there are more instruments and the sounds are louder.

One of the main challenges, Foo said, was considering the impact of the sounds he chose to associate with the data points. For example, he was cautious not to associate ‘wealth’ with sounds that could be construed as ‘happy’ or having higher quality sound.

“Each data set has consequences in how you make creative and ethical decisions,” he said.

He also had to learn to create in a “hands-off” way, resisting the natural urge of an artist to clean up the output and mould it the way he wanted it. “I had to draw a balance between being true to the data and not manipulating what the data set is saying.”

That meant designing the algorithm and then pressing play and listening to the song that was created, without first imagining the end product. This, Foo said, was a new way of approaching his art, as in the past the creation process was always visual and physical. Now, instead of anticipating the final outcome, he simply defined the rules by which it would be created.

In addition to ‘Two Trains’, Foo created songs for other data sets, including refugee data, brain wave data, and online dating data. While he is pleased with the feedback he received on these sounds experiments, Foo says he is keen to continue exploring different media and using the qualities of that media to deliver information in new ways.

“I’m interested in multi-sensory experiences, and sound is just another tool to use for that,” he says.

Putting data + evidence in the hands of girls’ & women’s movements, advocates & decision makers to reach the #GlobalGoals — www.equalmeasures2030.org

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