| Paper: | MLSP-P1.7 | ||
| Session: | Blind Source Separation II | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | A NEW DIAGONAL HESSIAN ALGORITHM FOR BLIND SIGNAL SEPARATION | ||
| Authors: | Maha Elsabrouty, Tyseer Aboulnasr, Martin Bouchard, University of Ottawa, Canada | ||
| Abstract: | A new algorithm for blind signal separation of speech signals that does not require pre-whitening is proposed in this paper. The algorithm is based on second order optimization using Riemannian geometry. The algorithm employs several practical approximations to the Hessian matrix of the maximum-likelihood blind separation cost function, to produce a computationally efficient algorithm that is capable of working on-line. Simulation results show the improved performance of the proposed algorithm with different mixing data. | ||
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