# SerumRNN: Step by Step Audio VST Effect Programming

arXiv: https://arxiv.org/abs/2104.03876 · HTML: https://mlsanity.com/paper/2104.03876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Deep Learning for Tube Amplifier Emulation](https://mlsanity.com/paper/1811.00334/) (2018)
- [InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals](https://mlsanity.com/paper/1812.06349/) (2018)
- [A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor](https://mlsanity.com/paper/1905.01022/) (2019)

## Built on by (most cited first, 3 of 3)

- [Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation](https://mlsanity.com/paper/2205.03043/) (2022)
- [Synthesizer Preset Interpolation using Transformer Auto-Encoders](https://mlsanity.com/paper/2210.16984/) (2022)
- [Modulation Discovery with Differentiable Digital Signal Processing](https://mlsanity.com/paper/2510.06204/) (2025)

## 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.