Neural networks for blind separation with unknown number of sources

Blind source separation problems have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the number of sour ces is typically assumed to be known in advance, but this does not usually hold in practical applications. In this paper, various neural network archi tectures and associated adaptive learning algorithms are discussed for hand ling the cases where the number of sources is unknown. These techniques inc lude estimation of the number of sources, redundancy removal among the outp uts of the networks, and extraction of the sources one at a time. Validity and performance of the described approaches are demonstrated by extensive c omputer simulations for natural image and magnetoencephalographic (MEG) dat a. (C) 1999 Elsevier Science B.V. All rights reserved.