App -> https://hafiyyandi.github.io/latent_space_sequencer/index.html Last week after class I tried training a performanceRNN model with my Yoko Shimomura with more steps (>30,000), but it didn’t give me a good generation without perpetually-sustained notes. Because of that failure and the past performances, for this final performance I want to create something that moves beyond generating musical phrases similar to my dataset. I decided to use MusicVAE.js to create music fro

In the past performances, I encoded and trained each track in my MIDI files (melody & harmony) separately. Then I play the generated tracks on the same time. But I think that was not the proper way of generating two-track music, because the melody and harmony should correlate. So for this assignment, I tried using Performance_RNN workflow to encode the dataset and train the model. Dataset I used the same dataset I trained my 2nd performance with: 20 MIDI files of Yoko Shimomu

In the first performance, I frankenstein-ed 3 of my favorite songs with Markov chain and generate a midi file as an output. For this performance, I essentially planned to do the same, but with added objectives of: (1) having a more meaningful data corpus, and (2) designing a performance with the generated music that makes sense. This project has been more of a proof-of-concept of a "duet" pipeline. (1) Meaningful data corpus Playing games was a huge part of my childhood, and