Data Art: The Sound of Music Charts

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A couple weeks ago, I came across an infographic made by the music blog Pitchfork about the gender breakdown of performers at 2017 music festivals. Male groups comprised 74% of performances, while female groups represented just 14% of acts. The remaining 12% contained had both genders.

I play guitar and write for a music blog, and while I do feel that as a woman, I am in the gender minority in both of those activities, I was still surprised by the extremity of those numbers. I decided to investigate more. Does the 2017 lineup represent a more extreme gender disparity than other musical venues or do men dominate popular music that starkly?

To begin to answer that question, I turned to two different charts: the Billboard Top 100 Artists of the Year, and Pitchfork’s Top 50 Albums of the Year. I chose these two different sources because they are each considered, to a certain degree at least, the authority in their respective musical realms—pop and indie. To start, I collected data from the last five years of charts on each site, making a CSV that included the year, artist name, ranking, and gender.

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I suspected before I started visualizing the data that the Billboard charts would have a greater discrepancy between genders than the Pitchfork charts. Pop music, I thought, would reflect less progressive values than alternative music, plus Billboard uses a more scientific method  in their rankings (streams, album purchases, etc.) than Pitchfork, which, as far as I can tell, is selected by editors who can subjectively consider factors such as diversity.


When I visualized each data set, though, I discovered that the ratio of male to female performers is similar. In the Billboard Charts, the breakdown is:

349 Male; 132 Female; 18 Both

In the Pitchfork chart, it is:

167 Male; 61 Female; 22 Both

In both of them, all-female acts make up about a quarter of performers, and acts with either all females or a mix make up about a third of acts.

I decided to represent each chart year on a separate line, showing each of the three categories with rectangles of different colors (red for female, purple for both, and dark grey—a kind of default negative space—for male). Here are the two chart sources shown together:

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In class last week, I loved the projects that we saw that represented data with sound, and I thought that it might suit this project well. It is about music, after all. Also, seeing the stretches of dark space is one thing, but I thought that hearing a series of notes that always seems to return to a low drone would make the point more effectively.

Using a timer with millis() and the Processing sound library to create a sine oscillator, I made a very boring electronic song with three distinct pitches. Here’s that sequence choreographed (without sound) on the Billboard chart:

Different Sound

Finally, because the sine oscillator doesn’t make the prettiest of sounds and because this project is about female musicians, I decided to play the chart on guitar. I like the idea that, as Jer has talked about throughout the course of the semester, labor—time or physical exertion—can be another expression of the data. In the time that it took to get the notes right, would the finger responsible for strumming the string that represents male groups grow tired? (Yes.) It also brings the semester full circle for me, as it returns to the abstracted, aestheticized data representations that we explored in the first unit.

Here’s the song—not much more exciting than the one with the sine oscillator but with more reverb—that ensued:



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