Conceptualizing Genre

Massachusetts-based tech company The Echo Nest is a leader in the realm of music data collection and analysis. Essentially, they’ve developed a database of millions of songs (adding up to over a trillion data points) and a platform that helps companies better understand music content, genre, and listener experience. Spotify uses their platform, for instance, when creating stations or suggesting personal recommendations based on listening activity. The extensive database also allows for some interesting visualization work. The most famous is a genre map by Glenn McDonald. He created website called Every Noise at Once that uses Echo Nest data to visualize over a thousand different genres of music, each one with an audio sample. This site counts 1492 genres at the time of this writing—see the note at the bottom of the main page for an up-to-date number (note also a number of additional options for sorting the data).

The project is interesting in part because of the number of genres. Billboard, by comparison, counts pop, country, rock, R&B/hip-hop (which includes rap), dance/electronic, Latin, christian/gospel, and some others that it considers minor genres including blues, holiday, classical, comedy, jazz, reggae and world. The specification of so many more genres and sub-genres suggests new trends in the production of music as well as entirely new cultures organized around listening to music. One enabling factor for this proliferation of generic categories is digital technology. Genres like vaporwave, as we learned earlier, are inspired by digital culture and also rely on digital tools for their production.

But digital technology plays another role in the genre map at Every Noise at Once: the site uses an algorithm to generate the scatter plot that organizes genres in relationship to one another. It’s not a precise science, but as McDonald explains, “in general down is more organic, up is more mechanical and electric; left is denser and more atmospheric, right is spikier and bouncier.” That explains why classical piano appears at the bottom while deep tech house appears at the top. In some cases, the algorithm identifies clusters of artists with a similar sound but no culturally identifiable sub-genre. For instance, he recently identified a “hyper-poppy strain of pop-punk/-emo/-screamo” that he decided to label Pixie. A reference to The Pixies? I hope not because if so I am very out of touch with their influence—not a single one of the bands grouped under Pixie is familiar to me.

What makes the algorithmic approach to determining genre novel besides—the fun visualization—is how it seems to invent genre. Conventional music genres rely on a mixture of cultural recognition and institutional history (which might involve music charts, festivals, and industry marketing among other factors). To see a more conventional account of popular genres, along with a genealogy of their relationships, check out Music Map. What’s different about Every Noise at Once? For one thing, it has less regard for history and more fidelity to the self-labeling practices of musical subcultures. In addition to cultural practices, it adds machine recognition of musical qualities (waveform and tempo analysis, from what I can tell, but surely others as well). Joining culture and machine reading, then, McDonald gives life to genres that may never find unified expression elsewhere. Perhaps more importantly, he drops the needle on many genres that will never receive any mainstream recognition. Just check out lowercase or skweee or deep filthstep to see why the site has gotten so much press.


One thought on “Conceptualizing Genre

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s