Double-plane sound field separation after sound pressure extrapolation using back propagation neural networks

For the influence of the window effect caused by the finite aperture and the wraparound error due to the discrete microphone array on the double-plane sound field separation, a hologram pressure extrapolation method using back propagation neural network is proposed. Firstly, the learning samples consist of the planar coordinates of measurement points inside the aperture and the corresponding sound pressures. They are used to train neural networks, leading to the function fitting between both parameters. Next, the planar coordinates outside the aperture are introduced into the networks to generate the corresponding pressures, achieving the finite aperture extrapolation. Finally, the virtual large aperture including the measurement data and the extrapolated result is introduced into the double-plane sound field separation processing. Theoretical analysis shows that compared to the conventional extrapolation methods, the proposed method does not require a priori knowledge and is easy to use, yet of strong anti-interference capability and high accuracy. Numerical simulations and experiments further verify the feasibility and effectiveness of this method.