论文标题
通过基于各种自动编码器的节奏发电机作为DAW插件的AI设计，以使音乐制作民主化
Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin
论文作者
论文摘要
利用深度学习的音乐发电技术取得了重大进展。但是，音乐家和艺术家在日常音乐制作练习中使用这些技术仍然很难。本文提出了一个基于{Kingma2014}（vae）基于节律的生成系统的变异自动编码器\ cite {kingma2014}（kingma2014}（kingma2014}（kingma2014}），音乐家只能通过选择目标MIDI文件来训练深度学习模型，然后与该模型产生各种节奏。作者已将系统作为DAW（数字音频工作站）的插件软件实现，即Ableton Live的实时设备的最大值。选定的专业/半专业音乐家和音乐制作人使用了该插件，他们证明了该插件是创造性音乐的有用工具。插件，源代码和演示视频可在线获得。
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.
