Music Language Modeling with Recurrent Neural Networks
yoavz.com
yoavz.com
Like other people already mentioned, the part that usually gets neglected is the overarching dramatic structure of a musical piece. Compare a complete shakespeare piece to a pile of randomly thrown together half-sentences.
I don't fully understand the fascination of music generation with some ai-neural-learning buzzword bingo technique that always gets kickstarted by dumb-force-analysing a human made music corpus to achieve it.
What in a musical sense is much more interesting is to generate _new_ music that cannot be composed by a human, and cannot be played by a human. That's playing to the strength of the machines. Sonification of large datasets, sonification of function behaviour. Sonification of the binary world, that's so different to ours. This is much more interesting than the 10th failed emulation of a simple folk song.
Nevertheless, as a students piece about programming neural networks, it's certainly ok, the presentation is nice, but the result is uninspiring, like building a car tire out of bananas, just because it's possible. Just let the folk songs belong to the actual folk.
As a side note: what would happen if the result were millions of super nice catchy folk tunes on a button press? Would it be the end of pop music as we know it? Maybe i redact my opinion.
generative music based on data from the 2014 US open [0], remixes by james murphy [1] (of lcd soundsystem).
slightly funny to me that ibm has such a full soundcloud page.
[0] https://soundcloud.com/ibm (non-remix versions further down the page)
[1] http://pitchfork.com/reviews/albums/20103-remixes-made-with-...
Maybe all the cases of 'music done with some neural-machine-learning presented as awful sounding midi piano renderings' are just there because music seems to be a universally liked phenomenon simply presented as pitch over time and its appealing to research students of this specific field of programming to take this as an anchor for their experiments.
As a general tip for these projects: If you take music as a main plot point, then learn some basic music dsp and render your experiment with well behaved sinewaves, or get some daw and put some nice preset sound to it - basically put some minimal effort to the actual musical presentation. Awful music is much more unbearable than awful graphics.
Definitely agree with you that the result of these experiments and others similar to it so far have produced unstructured and unenjoyable music, when compared to human-level compositions. I also agree that you could achieve similar results by some rule-based system involving random walks through a musical scale, common chord progressions, etc.
However, to me what's exciting about this and similar projects is that the network is learning these rules on it's own. The whole point of machine learning is that we don't have to explicitly state or even understand the underlying structure or music theory, because the algorithm figures it out on it's own. And sure, right now, it is only learning structure that we can codify manually in rules. But who's to say that as neural network techniques become more advanced, they won't be able to learn more abstract concepts such as the overarching dramatic structure of a musical piece?
I did a quick search, and I probably miss a lot, but I found these:
http://papers.nips.cc/paper/5655-deep-temporal-sigmoid-belie...
http://dl.acm.org/citation.cfm?id=2806383
http://gitxiv.com/posts/WEoQCj8hxHz6vPxe6/gruv-algorithmic-m... https://github.com/MattVitelli/GRUV
http://www.hexahedria.com/2015/08/03/composing-music-with-re... https://news.ycombinator.com/item?id=10028878
I haven't really looked into any of these, so I'm not sure about the differences. But it would be good if you cite some relevant other works and point out the differences.
It is an integral tool for my own music. My chamber music project is essentially a real-time electroacoustic music generator that analyzes microphone input. So, a soloist plays along and influences what the tool plays, and the two play in "harmony."
You can hear samples at http://www.sonicmultiplicities.net
But this also misses the point, since the strength of our digital machines is not generating the same old musical variation that we could aswell compose ourselve if we weren't so lazy, but to show us music and sounds that we cannot compose and play by hand. The machine music should tell something about itself, not try to mimic us.
It feels like it would be an achievable goal, given the right kind of training material.
Maybe hiring a session pianist for a few days to harmonise a bunch of key/tempo normalised jazz standards on a midi keyboard, so that the harmonisations and melody input are separate, labeled data?
It looks like this, you can do repeats and everything else:
\new Voice \with {
\consists "Ambitus_engraver"
} \relative c' {
\voiceTwo
es4 f g as
b1
}
What would be somewhat impressive is if it spontaneously figured out the note sequence I hear from observing its re-expression in bits and pieces from various jigs and folk pieces in its training set, kind of like this:
It would make more sense to force the leitmotif and generate the rest of the song instead of generating from a random note.
There's the "generated" music concept sort of like this, that basically creates the piece from zero to finished product. As in, there are tones and sounds and maybe some rhythm in it. Basically it makes a track. There was a post here recently about a 'brain support' music generation program/service thing, and I'm pretty sure the sounds they use would fit in the above description.
The other concept is "element" music generation. This would be a plug-in or software piece that works for a specific instrument. Apple's GarageBand has Drummer[1] and I've had good results using it so far. I think there are others on the market and different examples of a similar concept, like Instant Haus[2]. These aren't stand-alone music generation pieces, but resources upon which to build into a whole.
That current inner state shall as much as possible be un-colored by human prejudice and mood and so must the program be written (dilemma).
The human shall fully acknowledge its role as initiator to a universe of binary logic unfolding over time, thus enabling the machine to be the only composer, the only conductor and the only performer of itself without any further interference.