论文标题
TT-NET：基于双路线变压器的声场在球形谐波域中的音场翻译
TT-Net: Dual-path transformer based sound field translation in the spherical harmonic domain
论文作者
论文摘要
在基于球形谐波（SH）分析的当前声场翻译任务的方法中，基于添加剂定理的解决方案通常面临由大矩阵条件数字引起的奇异值的问题。球形径向功能的不同距离和频率对翻译矩阵稳定性的影响将影响所选点SH系数的准确性。由于上述问题，我们提出了基于双路变压器的神经网络方案。更具体地说，双路径网络是由沿频率和顺序轴的两个维度的自我发项式模块构建的。网络中引入了转换 - 平均 - 偶然层和放大层，该层为多个采样点和升级提供解决方案。数值模拟结果表明，翻译的工作频率范围和距离范围都扩大。通过提出的双路径网络获得更准确的高阶SH系数。
In the current method for the sound field translation tasks based on spherical harmonic (SH) analysis, the solution based on the additive theorem usually faces the problem of singular values caused by large matrix condition numbers. The influence of different distances and frequencies of the spherical radial function on the stability of the translation matrix will affect the accuracy of the SH coefficients at the selected point. Due to the problems mentioned above, we propose a neural network scheme based on the dual-path transformer. More specifically, the dual-path network is constructed by the self-attention module along the two dimensions of the frequency and order axes. The transform-average-concatenate layer and upscaling layer are introduced in the network, which provides solutions for multiple sampling points and upscaling. Numerical simulation results indicate that both the working frequency range and the distance range of the translation are extended. More accurate higher-order SH coefficients are obtained with the proposed dual-path network.
