Technical Program

Paper Detail

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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