A new single-mixture source separation method

Authors:
Yina Guo;Shuhua Huang
Affiliations:
Department of Electronics and Information Engineering, Taiyuan University of Science and Technology, ShanXi Taiyuan, China;Department of Electronics and Information Engineering, Taiyuan University of Science and Technology, ShanXi Taiyuan, China
Venue:
International Journal of Computer Applications in Technology
Year:
2013

Citing 3
Cited 0

Independent component analysis: algorithms and applications

Neural Networks
Source separation using single channel ICA

Signal Processing
Blind identification of multi-channel systems with single input and unknown orders

Signal Processing

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Abstract

In commonly used independent component analysis ICA-based methods for blind separation of single input multiple outputs, such as single channel ICA, wavelet ICA and ensemble empirical mode decomposition EEMD ICA, the prior knowledge of the signals is assumed to be known, the sources are assumed to be disjointing in the frequency domain or the main channels selection from multi-channel outputs is not automatic. A new method based on EEMD, principal component analysis PCA and ICA that makes no such assumptions is presented in this paper. EEMD describes any time-domain signal as a finite set of oscillatory components called intrinsic mode functions IMFs. PCA can reduce dimensions of IMFs by using orthogonal transformation. ICA finds the independent components by maximising the statistical independence of the dimensionality reduction IMFs. The separation performance of our algorithm is compared with EEMD-ICA through simulations. The experimental results show our method outperforms EEMD-ICA with lower relative root mean squared error RRMSE and higher cross-correlation.