[Haskell-cafe] data mining, or something like it
dennis.raddle at gmail.com
Tue May 30 04:50:49 UTC 2017
I have a general computer science question first, and then I will ask if
Haskell might be helpful for this.
I'm researching "algorithmic music composition," the construction of
musical compositions through choices made my algorithms. No piece of music
is ever 100% human-composed or 100% algorithmic -- so to be more clear, I'm
interested in researching "musical fitness functions" that produce good
results when searching the music space. Anything the computer composes will
be listened to by me, and only the most promising fitness functions will be
explored in greater depth.
So my general question is about the following idea --
So in the course of generating lots of musical examples, and rating them as
"good" or "bad," I'll end up with a database that can perhaps be "mined"
for new fitness functions. I would start with a question. A random example
of a question would be "is there a pattern in the density of dissonance,
such that more dissonance in certain places in the composition tends to
make it better (or worse)?" I would suspect, out of pure intuition, that it
does, but I may not know the exact details. For instance, I might not know
if dissonance matters more in certain places within a measure. So I could
code up something that looks for patterns among the good and bad musical
examples in my database to see if it can find something specific.
So is this called "data mining" or something else?
Second, are there any Haskell libraries that can help with this task?
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