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Single-Mixture Source Separation Using Dimensionality Reduction of Ensemble Empirical Mode Decomposition and Independent Component Analysis

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Abstract

Blind source separation of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Numerous blind source separation methods are commonly used for blind separation of single input multiple output. However, the priori knowledge of the signal is assumed to be known or the main channels selected from multi-channel output are not self-adaptive and automatic. Presented in this paper is a new method based on dimensionality reduction of ensemble empirical mode decomposition (EEMD), and ICA does not rely on such assumptions. The EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction IMFs. Principal component analysis (PCA) is applied to reduce dimensions of IMFs. The separated performance of EEMD-PCA-ICA algorithm is compared with EEMD-ICA through simulations, and experimental results show EEMD-PCA-ICA algorithm outperforms EEMD-ICA with higher cross-correlation and lower relative root mean squared error (RRMSE).

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Acknowledgements

Funding for this work was supported by 2010 research project of Shanxi Scholarship Council of China [No. 92] [No. 20101069], 2011 research project of Department of Human Resources and Social Security of Shanxi Province [No. 20121030] and 2010 Youth Foundation of Taiyuan University of Science and Technology of China [No. 20103004].

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  1. Taiyuan University of Science and Technology, ShanXi, Taiyuan, 030024, China

    Yina Guo, Shuhua Huang & Yongtang Li

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  1. Yina Guo
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  2. Shuhua Huang
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  3. Yongtang Li
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Correspondence to Yina Guo.

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Guo, Y., Huang, S. & Li, Y. Single-Mixture Source Separation Using Dimensionality Reduction of Ensemble Empirical Mode Decomposition and Independent Component Analysis. Circuits Syst Signal Process 31, 2047–2060 (2012). https://doi.org/10.1007/s00034-012-9414-1

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  • DOI: https://doi.org/10.1007/s00034-012-9414-1

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