To describe the sound radiation of the human voice into all directions, measurements need to be performed on a spherical grid. However, the resolution of such captured directivity patterns is limited and methods for spatial upsampling are required, for example by interpolation in the spherical harmonics (SH) domain. As the number of measurement directions limits the resolvable SH order, the directivity pattern suffers from spatial aliasing and order-truncation errors. We present an approach for spatial upsampling of voice directivity by spatial equalization. It is based on preprocessing, which equalizes the sparse directivity pattern by spectral division with corresponding directional rigid sphere transfer functions, resulting in a time-aligned and spectrally matched directivity pattern that has a significantly reduced spatial complexity. The directivity pattern is then transformed into the SH domain, interpolated to a dense grid by an inverse spherical Fourier transform and subsequently de-equalized by spectral multiplication with corresponding rigid sphere transfer functions. Based on measurements of a dummy head with an integrated mouth simulator, we compare this approach to reference measurements on a dense grid. The results show that the method significantly decreases errors of spatial undersampling and this allows a meaningful high-resolution voice directivity to be determined from sparse measurements.
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