Every Noise at Once sheds light on global music streaming habits, providing insights that are funny, fascinating, and sometimes disappointing.

In June of 2015, Spotify launched a new feature called “Discover Weekly.” Since then, these personalized, data-driven playlists — refreshed with a new set of 30 songs every Monday morning — have become one of the platform’s signature offerings. Likely one of Spotify’s most popular features, they have been especially successful among the particularly music-obsessed (according to Vox, listeners who engage with the playlists spend twice as much time on Spotify compared to users who don’t). Discover Weekly is popular for its eerie ability to determine your precise taste in music—no matter how unusual or unique it may be—and then cook you up a freshly-prepared buffet of recommendations once a week, like a close friend making you the perfect mixtape. 

The ‘mind’ behind Spotify’s ability to craft this customized menu is The Echo Nest, a company that builds music-analyzing algorithms and originally evolved out of the dissertation of two students from the MIT Media Lab. In 2014, Spotify bought The Echo Nest and has been putting it to work calibrating your Discover Weekly playlist (and the streaming giant’s other music recommendation features) ever since. User listening data — as well as algorithms built to collect, analyze, and use it — is the magic fairy dust that makes your Spotify account feel individual and relevant, and thus worth the monthly fee.

Yet more can be done with this data than just building personalized playlists — something that Glenn McDonald, principal engineer at The Echo Nest, is acutely aware of. He has used the mass amount of information generated each day by Spotify to produce the astonishing website Every Noise at Once, an online cornucopia of data-based musical insights. On its most basic level, Every Noise is a sprawling map of musical genres, organized to show the relationships between everything from “vapor twitch” to “deep melodic death metal,” from “Nigerian hip hop” to “alpine yodeling.” Users are able to navigate through the map and explore music they may never have otherwise heard, listening as they genre-hop across the audio landscape.

The Every Noise at Once genre map.
The Every Noise at Once genre map. Credit: Information is Beautiful Awards.

The website is also a treasure trove of insights into streaming data and Spotify users’ listening habits. One of the site’s many interesting features is Spotify Listening Patterns By Gender, which gives a glimpse into the gendered politics of the platform and exposes some compelling disparities in who is pressing play on which tracks. Across the application, only 22.9% of streams are from female or mixed-gender artists; more specifically, the algorithm has found that female listeners stream 31.2% from female or mixed-gender artists, while male listeners stream only 17.4%.

Another tool, called Every Place at Once, generates playlists based on what various cities are listening to. As of writing, inhabitants of Providence, RI listen to “pop rap” more than any other genre, and their streaming patterns are most similar to Dorchester, MA; residents of Buenos Aires enjoy “cumbia villera,” while folks in Singapore are into “mandopop.”

The site can also determine the tastes of students at a given college or university, displaying the results on a page titled Every School at Once. At the top of the selection tailored to India’s Amity University is “101” by Seedhe Maut; the sixth song on Brown University’s mix is “Slowly,” by Brown student band Orange Guava Passion. 

But wait! That’s not all. Have you been wondering what distinctive songs are enjoyed by 45- to 54-year-old men in Australia? Here’s a playlist for you. Or, should you prefer, a different playlist representing the music taste of the average Japanese woman over 65.

And if all that isn’t enough to satisfy your curiosity, Every Noise even features an algorithm for tracking worldwide levels of Christmas-music listening, year-round (check it out here). This happens to reveal some curious patterns. As McDonald writes on the page: “In the Philippines, and the other countries of the Filipino diaspora, the Christmas-music season opens with unmistakable decisiveness on September 1. I initially thought this was a data error. It is not. Other countries don’t get in the spirit until November 1, but after that Scandinavia quickly takes over.”

While it may be true that nothing can quite replace a perfect mixtape from a close friend, data-driven music discovery does have its own appeal. And arguably, Every Noise at Once in particular represents the best of what algorithms have to offer for music lovers: endless, curiously specific insights into what exactly is playing inside the world’s headphones.

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