Acoustic Structure Inverse Design and Optimization Using Deep Learning

TL;DR

This paper introduces a deep learning-based method for designing acoustic structures, significantly reducing the time and computational resources needed compared to traditional approaches, with demonstrated effectiveness on Helmholtz resonators.

Contribution

The work presents a novel deep learning approach for inverse design of complex acoustic structures, enhancing accuracy and efficiency over conventional numerical methods.

Findings

Accurate prediction of resonator geometries with multiple parameters

Improved optimization performance for desired acoustic properties

Potential applications in speech enhancement and sound insulation

Abstract

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.