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Open sphere microphone arrays (OSMAs) are simple to design and do notintroduce scattering fields, and thus can be advantageous than other arrays forimplementing spatial acoustic algorithms under spherical model decomposition.However, an OSMA suffers from spherical Bessel function nulls which make ithard to obtain some sound field coefficients at certain frequencies. This paperproposes to assist an OSMA for sound field analysis with physics informedneural network (PINN). A PINN models the measurement of an OSMA and predictsthe sound field on another sphere whose radius is different from that of theOSMA. Thanks to the fact that spherical Bessel function nulls vary with radius,the sound field coefficients which are hard to obtain based on the OSMAmeasurement directly can be obtained based on the prediction. Simulationsconfirm the effectiveness of this approach and compare it with the rigid sphereapproach.

Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural network