A method for sound field decomposition based on neural networks is proposed. The method comprises two stages: a sound field separation stage and a single-source localization stage. In the first stage, the sound pressure at microphones synthesized by multiple sources is separated into one excited by each sound source. In the second stage, the source location is obtained as a regression from the sound pressure at microphones consisting of a single sound source. The estimated location is not affected by discretization because the second stage is designed as a regression rather than a classification. Datasets are generated by simulation using Green's function, and the neural network is trained for each frequency. Numerical experiments reveal that, compared with conventional methods, the proposed method can achieve higher source-localization accuracy and higher sound-field-reconstruction accuracy.

提出了一种基于神经网络的声场分解方法，包括声场分离阶段和单源定位阶段，通过将多个源合成的麦克风上的声压分离为每个声源对应的激发声压，在单源定位阶段中，通过对单一声源的麦克风上的声压进行回归来获取源位置，由于第二阶段是回归而不是分类，因此估计的位置不受离散化的影响。使用Green's函数进行模拟生成数据集，每个频率都训练神经网络。数值实验表明，与传统方法相比，所提出的方法可以实现更高的源定位精度和更高的声场重建精度。

基于两阶段神经网络的声场分解