  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.
