Radial Acoustic Beamforming via Near-Field Array Processing in the Spherical Harmonics Domain

When two speakers are in the same direction but at different distances, far-field beamforming fails to isolate the desired speech signal. We develop two radial acoustic beamformers using near-field processing for spherical microphone arrays (SMAs). First, we present the near-field array signal model in both the spatial and spherical harmonics (SH) domains, where the steering vector decomposes into separate radial and angular components - a key aspect we exploit. For the first beamformer, the desired speech time-frequency (TF) coefficients are modelled as zero-mean circular complex Gaussian random variables with time-varying variances; for the second, a Laplacian distribution is used to enhance the modelling of the desired speech at the beamformer output. Both beamformers enforce a distortionless constraint on the desired source and employ maximum likelihood estimation (MLE) to iteratively update the beamformer weights and TF-domain variances. Simulation results show our methods outperform the conventional technique in near-field scenarios.