So, I have an awful taste in music. I am absolutely in love with this obscure type of techno called Happy Hardcore. Essentially, it’s underground British rave music. I don’t know how it happened, but it did, so I have to live it with.
Anyways! Ishkur’s guide to electronic music says that “ALL the world’s Happy Hardcore is made by only 12 guys, who have more pseudonyms than a shark has teeth, and who churn it out at such a feverish pace you’d almost think that there’s probably a program that makes it for them. Just randomize the key values, get Sugar from YTV to sing the lyrics, and away you go.”

I wanted to know if this is true.
Is my favorite genre of music controlled by 12 individuals who lack any sense of originality and who must rely on each other to create anything considered music?

I pulled a ton of data from Discogs (like IMDB for music). The data was poorly structured, and not very crawlable, so I had to do a bit of manual tinkering around to download a single artist. Once I had an artist’s real name, I could then pull their data (all their aliases and all the groups they were in) using the Discogs API, which returns some awfully structured XML…

The visualization itself shows three columns of information. The first shows every artist and a circle. The size of the circle represented their number of releases under their solo name and all their solo aliases. The third column had the artist, but this time their circle represented the number of tracks released by groups they were in. The middle column shows all the groups, again, sorted by number of tracks released by that group.

One of the main problems I had was many dimensions to my data, and wanting to show all of them. I had things like number of tracks, number of aliases, tracks per aliases, number of groups, tracks per group, members per group, and I probably could have figured a way to see who remixed who (but that might be a separate project…). In the end, I realized I needed to just focus on one piece: how artists were connected to each other through groups. Everything else at that point was peripheral. Unfortunately, I already had a substantial amount of code and couldn’t find myself starting over. I then was stuck with a visualization that wasn’t very scalable (there are still a lot more artists with a low quantity of releases that were not included).

Regardless, I think this visualization is a great start. I’d love to keep working with this data if I had more time, to further realize this vision.

Future Steps:
-Comparison should be much easier. I had a few mechanisms in place, but as the codebase grew, they broke and didn’t scale.
-I’m really interested in the difference of “inbredness” of different genres, so I’d love to throw a couple of these next to each other.
-The current visualization isn’t scalable at all. I think the best way to scale something like this would be to use something similiar to the “well formed eigenfactors” visualization we saw, though, I’m still unable to conceptualize how I could tie my data into that.

Download Source code and Data Files, as well as the song used 🙂