| Paper: | MLSP-P1.12 | ||
| Session: | Blind Source Separation and ICA | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | A BLIND SOURCE SEPARATION CASCADING SEPARATION AND LINEARIZATON FOR LOW-ORDER NONLINEAR MIXTURES | ||
| Authors: | Takayuki Nishiwaki; Kanazawa University | ||
| Kenji Nakayama; Kanazawa University | |||
| Akihiro Hirano; Kanazawa University | |||
| Abstract: | A network structure and its learning algorithm have been proposed for blind source separation applied to nonlinear mixtures. Nonlinearity is expressed by low-order polynomials, which are acceptable in many practical applications. A separation block and a linearization block are cascaded. In the separation block, the cross terms are suppressed, and the signal sources are separated in each group, which include its high-order components. The high-order components are further suppressed through the linearization block. A learning algorithm minimizing the mutual information is applied to the separation block. A new learning algorithm is proposed for the linearization block. Simulation results, using 2-channel speech signals, instantaneous mixtures, and 2nd-order post nonlinear functions, show good separation performance. | ||
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