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

Rigid-Body Sound Synthesis with Differentiable Modal Resonators