# Rigid-Body Sound Synthesis with Differentiable Modal Resonators

arXiv: https://arxiv.org/abs/2210.15306 · HTML: https://mlsanity.com/paper/2210.15306/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 27 Oct 2022.

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

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DiffusionNet: Discretization Agnostic Learning on Surfaces](https://mlsanity.com/paper/2012.00888/) (2020)
- [Lightweight and interpretable neural modeling of an audio distortion effect using hyperconditioned differentiable biquads](https://mlsanity.com/paper/2103.08709/) (2021)
- [Direct design of biquad filter cascades with deep learning by sampling random polynomials](https://mlsanity.com/paper/2110.03691/) (2021)
- [wav2shape: Hearing the Shape of a Drum Machine](https://mlsanity.com/paper/2007.10299/) (2020)
- [NeuralSound: Learning-based Modal Sound Synthesis With Acoustic Transfer](https://mlsanity.com/paper/2108.07425/) (2021)

## Built on by (most cited first, 5 of 5)

- [Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation](https://mlsanity.com/paper/2407.05516/) (2024)
- [Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman based Deep Learning Methods](https://mlsanity.com/paper/2408.16650/) (2024)
- [Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Plates](https://mlsanity.com/paper/2505.05940/) (2025)
- [Decomposer: Learning to Decompile Symbolic Music to Programs](https://mlsanity.com/paper/2607.01849/) (2026)
- [Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement](https://mlsanity.com/paper/2608.00667/) (2026)

## Abstract (arXiv)

Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods such as modal synthesis often rely on computationally expensive numerical solvers, while recent deep learning approaches are limited by post-processing of their results. In this work we present a novel end-to-end framework for training a deep neural network to generate modal resonators for a given 2D shape and material, using a bank of differentiable IIR filters. We demonstrate our method on a dataset of synthetic objects, but train our model using an audio-domain objective, paving the way for physically-informed synthesisers to be learned directly from recordings of real-world objects.