Mapping Your Music Collection

This is a really interesting article. A problem I've discussed on HN before is the "dynamic playlist."[0] Creating a music map like this provides a complete and elegant solution to this problem. Choose starting and ending tiles on the map, find any path between those tiles and you have your playlist. I might try my hand at implementing this soon.

In the past I had focused on using listener-generated data from sites like Last.fm, but if it turns out that musical similarity can be accurately determined using statistical methods that makes the whole thing much, much simpler.

[0] https://news.ycombinator.com/item?id=7630167

google cache: http://webcache.googleusercontent.com/search?q=cache:www.chr...

I was exploring this area recently, in a more specific way: I'd like to try to implement a DJ bot. But I am not sure machine learning is the best way: I would need to have a very big set of mix playlists by professional DJ to train a machine against. But then, if it worked, it would simply ape DJs. What I want to do is different: a "bot-flavored" playlist generator. A DJ that pushes tracks one after the other in an interesting way, but not in a human way. For instance, my bot-jockey could rightly decide to play a classical music track just after some deep house, because some features are matching. And this could be actually a good choice that a human DJ would not have done.

By the way, in the article, they avoid using external data, but for my project, I am trying to use AcousticBrainz data, and also explored Echonest's. Not sure where it will bring me though.

that reminds me of this project: MusicBox: Mapping and visualizing music collections (http://thesis.flyingpudding.com/)

there is a commercial product for unique ID provided by http://the.echonest.com/

While an interesting technique and premises, there are yet some oddities. Such as Beethoven in the middle of Rock songs (http://i.imgur.com/HgwuREl.png), I guess there are others.

I wonder how this would compare with Apple Genius, which I never used.

I get an "over quota" error :(

Any chance on releasing the code as a single script, so I don't need to copy-paste the snippets you posted and fill in the blanks myself?

So much cool things you can do with numpy and machine learning on python, this article makes me determined to play with it.

however, I don't know where to start. I'm not particularly good at math, calculus or statistics. to me, machine learning and numpy still sounds like stuff scientists are qualified to use, people with phds. regardless I'm amazed at the stuff like this article and inclined to venture in to it.