On Adaptive Noise Cancelling Based on Independent Component Analysis

نویسندگان

  • Hyung-Min Park
  • Sang-Hoon Oh
  • Soo-Young Lee
چکیده

We present a method to deal with adaptive noise cancelling based on independent component analysis (ICA). Although popular least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed ICA-based algorithm can utilize higher-order statistics. Additionally, extending to transform-domain adaptive filtering (TDAF) methods, normalized ICA-based algorithm is derived to improve convergence rates. Experimental results show that the proposed ICA-based algorithm provides much better performances than conventional LMS approach in realworld problems.

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تاریخ انتشار 2001