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As usual* the result sounds awfully unstructured and unenjoyable and could be aswell be achieved by some random walk through a musical scale, since if you put s
by iammyIP 11y ago
As usual* the result sounds awfully unstructured and unenjoyable and could be aswell be achieved by some random walk through a musical scale, since if you put some basic music theory in program form, you can get these small harmonic structures pretty easily.
(*since projects like this seem to pop up every couple of months)
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.
- arthur_pryor 11y agohere's something along those lines that i'd meant to check out but never got around to: https://soundcloud.com/ibm/sets/remixes-made-with-tennis-data https://soundcloud.com/ibm/sets/remixes-made-with-tennis-dat... 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 https://soundcloud.com/ibm (non-remix versions further down the page) [1] http://pitchfork.com/reviews/albums/20103-remixes-made-with-tennis-data/ http://pitchfork.com/reviews/albums/20103-remixes-made-with-...
- iammyIP 11y agoThat is certainly more musically interesting from a perspective of what can be achieved with computers, however the translation into electronic music of that tennis data involved lots of artistical freedom and it needs much more precise information on how that was translated. How else, if not by human interference, could there be some 4/4 techno beat and offbeat hihat. A much better interpretation would take the quite irregular time-based counting structure of tennis 'point'&'advantage' 'game''set' and interpret that as a rythm without the boring techno-bed. The sounds here are also much more fitting, since they are synthetic and adequately mixed. 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.
- deleted 11y ago[deleted]
- yoavz 11y agoOP here, you bring up some fair points and I wanted to respond. 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?