Machine-learning optimized method for regional control of sound fields

Abstract

Acoustic wave control is an important issue in living environment. Designing metasurface on scatterers is expected to control the sound field. However, an effective method to design the metasurface for large regional control is still lacking. Here we propose a machine-learning optimized method to solve problem of designing metasurface. According to the relationship between sound pressures at multiple points, convolutional neural network (CNN) is used to establish the mapping from local sound field to phase gradient of metasurface, which is further optimized by another CNN. The machine-learning method on designing metasurface has higher accuracy than the genetic algorithm. Using the machine-learning optimized method, not only the phase gradient of the metasurface can be obtained according to sound field, but also regional control of local sound field can be realized. For example, we can realize 8.37 dB intensification and 1.50 dB weakening of sound field at a square with a half-wavelength side. The metasurface designed by our proposed method is expected to realize noise reduction in large space, opening an avenue to achieve complex wave manipulation.