![]() If you want to categorize the music to "fix" the problem, you run into binning issues. Thus considering them is both right and wrong and we're back to our problem. Take for instance, Irish folk music where it may be signal and say, Acapella, where it may be noise. Qualities that matter in one genre, such as airy female vocals in a minor key, or whatever, are absolutely irrelevant for another genre. The musicologists do a serious study and have sophisticated tools I can't pretend to understand but their results sound similar: so similar that the serendipity and adventure is sucked out of it.Īlso they suffer from the generality problem as well. There's two approaches: musicologist and popularity. I've got tools for doing that and large labeled data sets I've been working on for 4 years. This task is certainly a nontrivial neural network problem.ĭoing it manually with human discretion works. I can do the music I like because I can narrow the ruleset but a general application is basically a winograd schema challenge because there's a large body of intuition required to weight the network. And then there's the "that's what I call music" type compilations where they're worthless. the clustering of those artists is a very strong high quality link. ![]() Take say the 1992 release Trancemaster 1. So do you follow the network of the guy playing the oboe? Maybe? Sometimes weird connections like the album artist is the strong link, sometimes it's compilation albums that one of the songs is placed on. Now if it's by an unknown artist, those links usually become high quality. Classical music for instance, may have 15 names on the credits, a jazz record may have like 4 labels it gets placed on. It's very much just a single user system that I haven't generalized because that's a way harder problem, the rules aren't generalizable. Choose it based on the people someone chose to work with and those networks. That's exactly what I've been working on.
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