Geometrically constraint ICA for convolutive mixtures of sound

The goal of this contribution is a new algorithm using independent component analysis with a geometrical constraint. The new algorithm solves the permutation problem of blind source separation of acoustic mixtures, and it is significantly less sensitive to the precision of the geometrical constraint than an adaptive beamformer. A high degree of robustness is very important since the steering vector is always roughly estimated in the reverberant environment, even when the look direction is precise. The new algorithm is based on FastICA and constrained optimization. It is theoretically and experimentally analyzed with respect to the roughness of the steering vector estimation by using impulse responses of real room. The effectiveness of the algorithms for real-world mixtures is also shown in the case of three sources and three microphones.