Neural network models for blind separation of time delayed and convolved signals
In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived fbr feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.
neural networks blind sources separation and deconvolution on-line adaptive algorithms