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 for 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.
|Number of pages||7|
|Journal||IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences|
|Publication status||Published - 1997|
- Blind sources separation and deconvolution
- Neural networks
- On-line adaptive algorithms