Acoustics Laboratory

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.