Blind Separation of Sources Using Density Estimation and Simulated Annealing

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概要

• 論文の詳細を見る

• This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

• 社団法人電子情報通信学会の論文
• 2001-10-01

著者

• Mansour A

  Ensieta Brest Fra

• PUNTONET Carlos

  the Dept. of Architecture and Computer Technology, University of Granada

• MANSOUR Ali

  Bio-Mimetic Control Research Center (RIKEN)

• Puntonet Carlos

  The Dept. Of Architecture And Computer Technology University Of Granada

関連論文

• Blind Separation of Sources Using Density Estimation and Simulated Annealing
• Blind Separation of Sources : Methods, Assumptions and Applications(Special Section on Digital Signal Processing)

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