biaxial-rnn-music-composition

Note that MIDI is a lot more effective when it comes to ML/AI, since it's multiple orders of magnitude less data. Daniel D. Johnson's (formerly known as Hexahedria, hired by Google Brain) model biaxial-rnn-music-composition is from 2015, requires very few resources for training or inference, and still delivers compelling, SOTA-or-close results wrt. improvising ("noodling") classical piano. https://github.com/danieldjohnson/biaxial-rnn-music-composit... You may also want to check out user kpister's recent port to Python 3.x and aesara: https://github.com/kpister/biaxial-rnn-music-composition (Hat tip: https://news.ycombinator.com/item?id=30328593 ). Music generation from notation is pretty much the MINST toy-scale equivalent for sequence/language learning models, it's surprising that there's so little attention being paid to it despite ho

You can generate very interesting music simply by working with MIDI as opposed to sampled audio (slashing complexity by orders of magnitude!) and starting from a good model architecture. Daniel D. Johnson's (formerly known as Hexahedria, hired by Google Brain) model biaxial-rnn-music-composition is from 2015, requires very few resources for training or inference, and still delivers compelling, SOTA-or-close results wrt. improvising ("noodling") classical piano. Github https://github.com/danieldjohnson/biaxial-rnn-music-composit... , you may also want to check out user kpister's recent port to Python 3.x and aesara: https://github.com/kpister/biaxial-rnn-music-composition (Hat tip: https://news.ycombinator.com/item?id=30328593 )

I was inspired by your comment and took a look at Daniel's work. Forked it and spent the day upgrade to Aesara (the maintained Theano fork) and python3. Haven't fully trained/tested the model, but feel free to take a look: https://github.com/kpister/biaxial-rnn-music-composition