A signal-dependent quadratic time frequency distribution for neural source estimation

  • Authors:

  • Pu Wang;Jianyu Yang;Zhi-Lin Zhang;Gang Wang;Quanyi Mo

  • Affiliations:

  • School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Blind Source Separation Research Group, University of Electronic Science and Technology of China, Chengdu, P.R. China;Blind Source Separation Research Group, University of Electronic Science and Technology of China, Chengdu, P.R. China;Blind Source Separation Research Group, University of Electronic Science and Technology of China, Chengdu, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain.