In this project we attempt to use a combination of methods of manipulating the input midi note sequences such as separating and combining instruments, chunking the song into sections and interpolating the sections, and recombining interpolations of multiple songs to then feed the interpolated output as input for new music generation using MusicRNN. The same inputs will always generate the same output. MusicVAE interpolation is deterministic, meaning This naturally means the middle element of the array is the most evenly Interpolation essentially formsĪ combination of the 2 inputs and produces an array of outputs which areĪ range of monophonic music from most similar to input 1 to most similar to MusicVAE's main feature is interpolation of 2 individual midi inputs (alsoįormatted as quantized note sequences). MusicVAE (Variational Auto Encoder) overview: It's algorithm is based on the model checkpoint used for initializing the MusicRNN model. The output is non-deterministic, so it can produce new sounds each time it generates even if the input is the same. This is the key tool used in our project to generate new musical tracks. MusicRNN's current primary feature and function is generation of monophonic output given a single midi input (formatted as a quantized note sequence).
MusicVAE MusicRNN (Recurrent Neural Network) overview: Music components, primarily two Magenta subprojects: MusicRNN and At the end of 2016, they published an LSTM model tuned with Reinforcement Learning. They have developed several models to generate music. Magenta is for both music and graphic art but for this project we focus only on the This project developed by Google Brain aims at creating a new tool for artists to use when working and developing new songs.
Magenta.js naturally is built with Tensorflow.js and that is what this project uses. Magenta uses machine learning based on TensorFlow which also has a javascript based version for working in the browser. Magenta is a creative research project started by Google and contributed to by other non-affiliated hobbyists, creatives, artists, and programmers. This project is primarily built and based on the tools and frameworks created by the Magenta Project.
This experiment focuses on the synthwave style of the band Devo. This research project's goal is to experiment with machine learning to generate new music mimicing a musical style. See the project live: Music/Machine Learning experimental research project inspired by Prof.
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