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Independent Component Analysis for Simultaneous Active Noise Canceling and Blind Signal Separation

  • Hyung Min Park
  • Tae-Su Kim
  • Yoon-Kyung Choi
  • Soo-Young Lee
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

A new algorithm is presented to perform active noise canceling and blind signal separation simultaneously. In many real-world problems speech signal is contaminated by noises, some of which are completely unknown while the other may be estimated by microphones located near the noise sources. Electric ‘line’ signals of audio equipments may also be used to estimate the noises. The active noise canceling removes the estimated noises with reverberation, while blind signal separation extracts speech signal from unknown noisy mixtures. Both algorithms are based on independent component analysis (ICA), which assumes statistical-independence among acoustic sources. The ICA-based active noise canceling utilizes higher-order statistics, and outperforms the standard least-meansquare (LMS) algorithm with quadratic statistics in real-world applications.

Keywords

Independent Component Analysis Speech Signal Independent Component Analysis Speech Enhancement Adaptive Noise Cancel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hyung Min Park
    • 1
  • Tae-Su Kim
    • 1
  • Yoon-Kyung Choi
    • 2
  • Soo-Young Lee
    • 1
    • 2
  1. 1.Korea Advanced Institute of Science and TechnologyBrain Science Research CenterDaejeonKorea
  2. 2.Extell Technology CorporationSeoulKorea

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