This is part 3 of my explorations of using deep learning for assisting the process of music composition. In this part, I look at creating things other than strict folk tunes with a model trained by deep learning methods on over 23,000 folk tunes. Part 1 is here. Part 2 is here.
Does the world really need tens of thousands of new reels and jigs? Maybe or maybe not; but my main motivations for composition are to create musical experiences, solve puzzles, learn, and be funny/dramatic. Toward these ends, I am finding that this music generation system can provide a wealth of materials and ideas. Here are some examples.
The system under study generated this curious little output:
Not a tiptop imitation when it comes to Western folk music, but it immediately brought to my mind drum and fife music, as well as music like that performed by Indian brass bands like the great Jaipur Kawa Brass Band. So, with a little reorchestrating, editing, and effects, we transform it into something like a passing marching band:
The system generated another failed emulation of Western folk music:
and it gave me the idea to create an antiphonal duet. I enjoy the improvisatory feeling of the playing.
Our model generated a piece it calls, “A Fhsoilah Kilnie”, which is made a right mess by the guitarist and flutist.
I don’t know what the system was “thinking.” However, administering some major changes with my certified artistic license and now we have a serious piece with integrity. Bonus: it’s dancable for very agile penguins and the occassional grumpy elephant seal.
Finally, when something tells me to listen to a piece titled, “A Bump Of Howled Sho The fetch”, I have the expectation of something dramatic. Our system generated such a piece, to which our session performers do no justice.
Instead, I layer all of my favorite sounds, and then layer them again but amplified, to make a real big bump of howled shoing all fetches everywhere.
No doubt those fetches are now shoed by a massive bump of howled.