Adaptive Natural Gradient Algorithm for Blind Convolutive Source Separation

Authors:
Jian Feng;Huaguang Zhang;Tieyan Zhang;Heng Yue
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang 110004, China and Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Sh ...;School of Information Science and Engineering, Northeastern University, Shenyang 110004, China and Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Sh ...;School of Information Science and Engineering, Northeastern University, Shenyang 110004, China;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang 110004, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

An adaptive natural gradient algorithm for blind source separation based on convolutional mixture model is proposed. The proposed method makes use of cost function as optimum criterion in separation process. The update formula of separation matrix is deduced. The learning steps for blind source separation algorithm are given, and high capability of the proposed algorithm has been demonstrated. The simulations results have shown the validity, practicability and the better performance of the proposed method. This technique is suitable for many applications in real life systems.