- Beamforming using spherical arrays has become increasingly popular in recent years. However, the performance of beamforming algorithms is greatly affected by the limited number of sensors. This work offers a novel approach based on pre-processing of the spatial data in order to better separate the signal from noise, thus improving beamforming performance. The method involves transformation of the data to the spatio-spectral domain, using the spatially-localized spherical Fourier transform, followed by masking. The masking function is defined using a-priori knowledge of signal to noise ratio. The performance of the proposed algorithm is then evaluated using a simulation study, showing improvement over conventional spatial filtering.