| Paper: | MLSP-P5.1 | ||
| Session: | Blind Source Separation III | ||
| Time: | Friday, May 19, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Blind Signal Separation Using a Criterion Based on Principle of Minimal Disturbance | ||
| Authors: | Uttachai Manmontri, Patrick Naylor, Imperial College London, United Kingdom | ||
| Abstract: | The concept underlying most on-line gradient-based algorithms for blind signal separation (BSS) is that the unknown demixing matrix is adjusted with an appropriate step-size in the direction of the gradient computed at each sample instant. Associated with these algorithms is a gradient noise problem. In this paper, we develop, from the on-line processing (OP) algorithm derived using the nonstationarity and nonwhiteness properties, a normalized algorithm in which the update of the demixing matrix is based on the minimal disturbance principle. We show that the resulting updates are in the same direction as those of the original algorithm but with a scaling factor whose upper bound is unity. We evaluate the convergence speed and robustness to gradient noise of the new algorithm. | ||
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