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Every noise at once: algorithms in the music industry and microgenre

The prevalence of streaming services and the internet have changed the landscape of music. TikTok acts as a powerful promotional tool—if your song can soundtrack a trend or get picked up by a dance challenge, that virality translates to more listens than could ever before be achieved by small and independent artists. Spotify holds the largest market share for free music streaming platforms, and its algorithms are hugely influential for popular music. Last year, I went to a concert and the opener commented that Spotify had put one of his songs on an indie music playlist, and it quickly became his most popular song. Spotify is constantly collecting data from listeners that develops its personalized recommendations, and the extent of its data collection is no surprise to anyone who explores their Spotify Wrapped. It uses this information to recommend similar artists to those that the listener may enjoy, create radios off of songs, and give suggestions on Discover Weekly. By sorting listeners into groups like this, Spotify inadvertently creates microgenres, or large groups of listeners with specifically similar tastes. The concept of the microgenre was not created from digital music, but the internet has developed ways for the people within these subsets to find each other easily. 

Every Noise at Once is a data visualization tool that takes information from Spotify and attempts to make the scale of microgenres comprehensible. The creator of the website, Glenn McDonald, explains that “The point of the map, as with the genres, is not to resolve disputes but to invite you to explore music. It is an attempt—however uneven, idiosyncratic, and incomplete—to embrace this new state of the world, in which nearly all of humanityʼs recorded music is streamable or downloadable, and give you a way to find out what you donʼt know you donʼt know.”  It’s a really interesting website to explore; the home page is just a spread of all the microgenres with examples and clicking on any of the genres leads you to a map of all the artists listed under the genre. I enjoy searching for a genre I like and listening to clips of songs from that genre I may have otherwise never heard before. At the bottom of the page, there is a list of other plots that give different ways to interact with the data. For example, Genres in Their Own Words finds the most common words used in song titles, alphabetically sorted by genre, and Every School at Once shows the most common genres of larger colleges.

Algorithms like Spotify’s are designed to keep you listening, either so that you continue listening to ads to get to the music or subscribe for ad-free content, both of which make the company money. While algorithmic recommendations can work well, what’s the point of having access to 70 million songs if you only listen to the Top 100? McDonald explains the beauty of these visualization tools by saying, “We make maps to mark treasure when we think treasure is rare, and then, later, to remember where weʼve been, once we start to realize that there are treasures everywhere.” I encourage you to check out Every Noise at Once and see if you can stumble across a hidden gem. 

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