Sound field decomposition based on two-stage neural networks
This addresses sound field analysis for applications like audio processing, but it is incremental as it builds on existing neural network approaches with a specific two-stage design.
The paper tackles sound field decomposition by proposing a two-stage neural network method that separates sound pressure from multiple sources and then localizes each source via regression, achieving higher source-localization and sound-field-reconstruction accuracy compared to conventional methods.
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.