  We present a novel learning-based modal sound synthesis approach that
includes a mixed vibration solver for modal analysis and an end-to-end sound
radiation network for acoustic transfer. Our mixed vibration solver consists of
a 3D sparse convolution network and a Locally Optimal Block Preconditioned
Conjugate Gradient module (LOBPCG) for iterative optimization. Moreover, we
highlight the correlation between a standard modal vibration solver and our
network architecture. Our radiation network predicts the Far-Field Acoustic
Transfer maps (FFAT Maps) from the surface vibration of the object. The overall
running time of our learning method for any new object is less than one second
on a GTX 3080 Ti GPU while maintaining a high sound quality close to the ground
truth that is computed using standard numerical methods. We also evaluate the
numerical accuracy and perceptual accuracy of our sound synthesis approach on
different objects corresponding to various materials.
