Thank you Alec for posting this wonderful example of the application of RNNs to Midi music files. It is something I have wanted to do myself. Another topic I think would be valuable might be to classify various midi files in terms of various underlying musical patterns and then use those patterns to create new music by modifying the patterns using techniques like inversion, transposition etc. Do you know of any work in that area using MMA?

regards Michael

I was surprised how pleasant these music tracks actually are. Thank you for sharing! How much time did this project take? Also I am curious about data -- did you publish your dataset, maybe on that GitHub repo? Is this all modern-classic composers based or there is some old classical music too for training? Any famous composers?

I built my dataset from a collection of my favorite piano scores from musescore. You want a lot of different .mid files so the network you train cannot simply memorize all of your training data and instead has to perform some amount of information generalization. Our data augmentation strategy helps with this, but it will not completely solve the issue of having too little data. I have found that 150 songs is acceptable when using smaller RNNs.

BTW about repos. You might consider for your work Wolfram's Neural Net and Data Repositores:

• https://resources.wolframcloud.com/NeuralNetRepository
• https://datarepository.wolframcloud.com

Also, many works on MuseScore.com have been published into the public domain, including many great classical compsers' works. These public domain works can be downloaded for free without a 'pro' license since there are no royalties to pay, and they could form the basis of a dataset that could be freely distributed.

Such a dataset could be uploaded to the Wolfram Data Repository without issue!