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Journal of Applied Sciences
  Year: 2015 | Volume: 15 | Issue: 9 | Page No.: 1158-1166
DOI: 10.3923/jas.2015.1158.1166
 
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Blind Source Separation of Mixed Noisy Audio Signals Using an Improved FastICA

Aws Al-Qaisi

Abstract:
Independent Component Analysis (ICA) is a powerful Blind Source Separation (BSS) technique, which is an interesting method applied to many applications in engineering. In this study, a novel treatment of noisy mixture is implemented by developing an improved FastICA algorithm with an optimized and adaptive step size. The proposed algorithm is implemented in three steps: Centering, whitening and independent component separation. Whitening step is adjusted to deal with noisy mixtures. The performance of the proposed algorithm is compared with RobustICA. Experimental results reveal that the proposed algorithm achieved better MSE than RobustICA on different SNR ranges by 62%.
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How to cite this article:

Aws Al-Qaisi , 2015. Blind Source Separation of Mixed Noisy Audio Signals Using an Improved FastICA. Journal of Applied Sciences, 15: 1158-1166.

DOI: 10.3923/jas.2015.1158.1166

URL: https://scialert.net/abstract/?doi=jas.2015.1158.1166

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