We propose a new algorithm for blind source separation (BSS),
in which independent component analysis (ICA) and beamforming
are combined to resolve the low-convergence problem
through optimization in ICA. The proposed method consists of
the following two parts: frequency-domain ICA with
direction-of-arrival (DOA) estimation, and null beamforming
based on the estimated DOA. The alternation of learning
between ICA and beamforming can realize fast- and high-convergence
optimization. The results of the signal separation experiments
reveal that the signal separation performance of the proposed
algorithm is superior to that of the conventional ICA-based BSS method.
