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.

翻译：提出了一种基于神经网络的声场分解方法。该方法包含两个阶段：声场分离阶段和单声源定位阶段。在第一阶段中，将多个声源在麦克风处合成的声压分解为各声源单独激发的声压。在第二阶段中，通过回归方法从仅含单个声源的麦克风声压数据中获取声源位置。由于第二阶段被设计为回归而非分类任务，因此估计位置不受离散化影响。数据集利用格林函数仿真生成，并为每个频率分别训练神经网络。数值实验表明，与传统方法相比，本方法能够实现更高的声源定位精度和声场重建精度。