基于深度学习的低频宽带隔声器件设计*

在实际应用中,通常需要将多个声人工结构单元进行组合来实现低频宽带的隔声降噪。这种组合结构往往参数较多,传统的设计方法很难对其进行高效的自动化设计。本文在集总参数模型的基础上,提出了一种基于深度学习的低频宽带隔声器件设计方法,并基于该方法完成了由9个二阶亥姆霍兹共鸣器单元组合而成的低频宽带隔声装置的设计。仿真结果表明,该隔声装置在158 Hz~522 Hz范围内均具有良好的隔声效果,从而验证了所提出方法的有效性。与传统方法相比,本文所提出的设计方法不仅减少了对设计者专业知识和设计经验的依赖,而且具有更高的设计效率,更强的通用性,未来有望进一步推广至其他声人工结构的设计领域。

Low-frequency Broadband Sound Insulation Device Design Method Based on Deep Learning

In practical applications, it is necessary to combine several acoustic structures to realize broadband sound insulation in the low frequency range. Considering that the composite structures often have multiple parameters, it is difficult to design them efficiently using the traditional design methods. In this paper, we proposed a design method of the low-frequency broadband sound insulation device using deep learning model based on the lumped-parameter technique. Moreover, we design a composite structure with 9 two-order Helmholtz resonators using the proposed method. The simulation results show that the composite structure has good sound insulation effect in the range of 158 Hz ~ 522 Hz, which demonstrates the effectiveness of the proposed method. Compared with the traditional methods, the proposed method can not only reduce the dependency on the designer"s skills and experiences, but also improve design efficiency. The proposed model has a strong versatility and scalability, which can be further extended to other acoustic structures.