Sound field decomposition based on two-stage neural networks
Ryo Matsuda Makoto Otani
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.