Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 59 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 40 tok/s Pro
GPT-5 High 27 tok/s Pro
GPT-4o 104 tok/s Pro
Kimi K2 195 tok/s Pro
GPT OSS 120B 467 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin (2004.01525v1)

Published 1 Apr 2020 in eess.AS, cs.LG, and cs.SD

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 Autoencoder\cite{Kingma2014}(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.

Citations (8)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Authors (1)