  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.
