# InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals

arXiv: https://arxiv.org/abs/1812.06349 · HTML: https://mlsanity.com/paper/1812.06349/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Deep Integrated Explanations](https://mlsanity.com/paper/2310.15368/) (2023)
- [DiffMoog: a Differentiable Modular Synthesizer for Sound Matching](https://mlsanity.com/paper/2401.12570/) (2024)
- [Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching](https://mlsanity.com/paper/2506.07199/) (2025)
- [Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models](https://mlsanity.com/paper/2310.16584/) (2023)
- [SerumRNN: Step by Step Audio VST Effect Programming](https://mlsanity.com/paper/2104.03876/) (2021)
- [Synthesizer Preset Interpolation using Transformer Auto-Encoders](https://mlsanity.com/paper/2210.16984/) (2022)
- [White-box Audio VST Effect Programming](https://mlsanity.com/paper/2102.03170/) (2021)
- [Subtractive Modulative Network with Learnable Periodic Activations](https://mlsanity.com/paper/2602.16337/) (2026)
- [Diff2Mix: Controllable Music Mixing via Diffusion Models and Differentiable Audio Effects](https://mlsanity.com/paper/2608.05442/) (2026)
- [Evaluating Loss Functions in Differentiable Out-of-Domain Sound-Matching with Partial Parameter Distance](https://mlsanity.com/paper/2608.27698/) (2026)
- [Off-manifold robustness in synthesizer inversion with joint distribution flow matching](https://mlsanity.com/paper/2609.29320/) (2026)

## Abstract (arXiv)

Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input sound. InverSynth is based on strided convolutional neural networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness InverSynth is demonstrated on a subtractive synthesizer with four frequency modulated oscillators, envelope generator and a gater effect. We present extensive quantitative and qualitative results that showcase the superiority InverSynth over several baselines. Furthermore, we show that the network depth is an important factor that contributes to the prediction accuracy.