# Learning Control of Neural Sound Effects Synthesis from Physically Inspired Models

arXiv: https://arxiv.org/abs/2503.08806 · HTML: https://mlsanity.com/paper/2503.08806/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning](https://mlsanity.com/paper/2506.03088/) (2025)
- [PhysicsSliders: Learning Physical Controls for Audio Foley Generation](https://mlsanity.com/paper/2610.18597/) (2026)

## Abstract (arXiv)

Sound effects model design commonly uses digital signal processing techniques with full control ability, but it is difficult to achieve realism within a limited number of parameters. Recently, neural sound effects synthesis methods have emerged as a promising approach for generating high-quality and realistic sounds, but the process of synthesizing the desired sound poses difficulties in terms of control. This paper presents a real-time neural synthesis model guided by a physically inspired model, enabling the generation of high-quality sounds while inheriting the control interface of the physically inspired model. We showcase the superior performance of our model in terms of sound quality and control.