Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin

Code

• github.com/sfc-computational-creativity-lab/RhythmVAE_M4L
  Officialnone★ 6
• github.com/naotokui/RhythmVAE_M4L
  none★ 244

Abstract

There has been significant progress in the music generation technique utilizing deep learning. However, it is still hard for musicians and artists to use these techniques in their daily music-making practice. This paper proposes a Variational AutoencoderKingma2014(VAE)-based rhythm generation system, in which musicians can train a deep learning model only by selecting target MIDI files, then generate various rhythms with the model. The author has implemented the system as a plugin software for a DAW (Digital Audio Workstation), namely a Max for Live device for Ableton Live. Selected professional/semi-professional musicians and music producers have used the plugin, and they proved that the plugin is a useful tool for making music creatively. The plugin, source code, and demo videos are available online.

Tasks

Reproductions