  From ancient to modern times, acoustic structures have been used to control
the propagation of acoustic waves. However, the design of the acoustic
structures has remained widely a time-consuming and computational
resource-consuming iterative process. In recent years, Deep Learning has
attracted unprecedented attention for its ability to tackle hard problems with
huge datasets, which has achieved state-of-the-art results in various tasks. In
this work, an acoustic structure design method is proposed based on deep
learning. Taking the design of multi-order Helmholtz resonator for instance, we
experimentally demonstrate the effectiveness of the proposed method. Our method
is not only able to give a very accurate prediction of the geometry of the
acoustic structures with multiple strong-coupling parameters, but also capable
of improving the performance of evolutionary approaches in optimization for a
desired property. Compared with the conventional numerical methods, our method
is more efficient, universal and automatic, which has a wide range of potential
applications, such as speech enhancement, sound absorption and insulation.
