SerumRNN: Step by Step Audio VST Effect Programming
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 8 Apr 2021.
Builds on
6 of 19 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
- 2020DDSP: Differentiable Digital Signal Processingcited by 177
- 2018Deep Learning for Tube Amplifier Emulationcited by 18
- 2018InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signalscited by 11
- 2019A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressorcited by 7
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1.
3 papers.