Acoustic Structure Inverse Design and Optimization Using Deep Learning
This work addresses the problem of acoustic structure design for applications like speech enhancement and sound absorption, representing an incremental improvement by applying deep learning to a known bottleneck.
The paper tackles the time-consuming and resource-intensive design of acoustic structures by proposing a deep learning-based method, which accurately predicts geometry and improves optimization performance, demonstrating efficiency and universality compared to conventional numerical methods.
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.