Abstract
In this paper, we show how the recently-developed Kuic-Net method for instantaneous blind source separation can be extended to the blind deconvolution task. The proposed algorithm has a simple form and is effective in deconvolving source signals with non-zero kurtoses from a linear filtered version of the source sequence. We then combine the natural gradient search technique with the KuicNet algorithm to enhance its convergence properties. Simulations verify the useful behavior of the proposed algorithms in deconvolving sources with various distributions.
| Original language | English (US) |
|---|---|
| Pages | 3-11 |
| Number of pages | 9 |
| State | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl Duration: Aug 31 1998 → Sep 2 1998 |
Other
| Other | Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII |
|---|---|
| City | Cambridge, Engl |
| Period | 8/31/98 → 9/2/98 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Software
- Electrical and Electronic Engineering