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Interesting to see that we're a long way from this technique actually generating anything valuable. E.g. if you try the LSTM Music Maker they link to (https://
by boffinism 9y ago
Interesting to see that we're a long way from this technique actually generating anything valuable.
E.g. if you try the LSTM Music Maker they link to (https://www.sentient.ai/sentient-labs/ea/lstm-music/ https://www.sentient.ai/sentient-labs/ea/lstm-music/) and enter a melody, the resulting 'improvisation' will neither have any of the hallmarks of your initial input nor obey any of the conventions of any genre of music I recognise. It'll just spew out a random-seeming spray of notes. Compared to, say, JukeDeck (https://www.jukedeck.com/make/track-generator/essential https://www.jukedeck.com/make/track-generator/essential), which uses bog-standard AI, it's got a long way to go.
- TheOtherHobbes 9y agoEvolutionary approaches won't necessarily produce better music. This is a well-researched area. The challenge with all GA work is finding the right fitness function. If you don't understand your domain well enough to define a good fitness function you're going to be wasting your time, and no amount of algorithmic and/or AI magic is going to help you.
- maksimum 9y agoI think the point here is that we don't need to define a fitness function for "good music", but a fitness function for e.g. "reconstruction error" for auto-encoders. While the latter is still challenging, it seems slightly easier, although I'm not sure that it's significantly easier than general manifold learning...