English

SerumRNN: Step by Step Audio VST Effect Programming

Sound 2021-04-12 v1 Machine Learning Audio and Speech Processing

Abstract

Learning to program an audio production VST synthesizer is a time consuming process, usually obtained through inefficient trial and error and only mastered after years of experience. As an educational and creative tool for sound designers, we propose SerumRNN: a system that provides step-by-step instructions for applying audio effects to change a user's input audio towards a desired sound. We apply our system to Xfer Records Serum: currently one of the most popular and complex VST synthesizers used by the audio production community. Our results indicate that SerumRNN is consistently able to provide useful feedback for a variety of different audio effects and synthesizer presets. We demonstrate the benefits of using an iterative system and show that SerumRNN learns to prioritize effects and can discover more efficient effect order sequences than a variety of baselines.

Cite

@article{arxiv.2104.03876,
  title  = {SerumRNN: Step by Step Audio VST Effect Programming},
  author = {Christopher Mitcheltree and Hideki Koike},
  journal= {arXiv preprint arXiv:2104.03876},
  year   = {2021}
}

Comments

Audio samples of the system can be listened to at bit.ly/serum_rnn

R2 v1 2026-06-24T00:58:18.734Z