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2013•Unpublished venueRequires access

Application Studies on Voice Signal Blind Separation of Independent Component Analysis

Peng Zhang, Wenjuan Li, Ceng Li, Guohua Wang, Hui-xian Chen, Qiying Wang

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Abstract

The Independent Component Analysis (ICA) method has applied in the field of blind source separation. On the basis of analyzing ICA, the study ameliorates the FastICA. The conventional FastICA has only a second order convergence rate and combination of static model and batch optimization algorithm. An improved ICA algorithm is therefore proposed to reduce the iteration steps and dynamic the algorithm. The experimental results show that the improved algorithm achieved satisfactory results.

About this research paper

What this paper is about

The Independent Component Analysis (ICA) method has applied in the field of blind source separation. On the basis of analyzing ICA, the study ameliorates the FastICA. The conventional FastICA has only a second order convergence rate and combination of static model and batch optimization algorithm. An improved ICA algorithm is therefore proposed to reduce the iteration steps and dynamic the algorithm. The experimental results show that the improved algorithm achieved satisfactory results.

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Key contribution

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Method / approach

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Available abstract

The Independent Component Analysis (ICA) method has applied in the field of blind source separation. On the basis of analyzing ICA, the study ameliorates the FastICA. The conventional FastICA has only a second order convergence rate and combination of static model and batch optimization algorithm. An improved ICA algorithm is therefore proposed to reduce the iteration steps and dynamic the algorithm. The experimental results show that the improved algorithm achieved satisfactory results.

Key concepts: Independent component analysis, FastICA, Blind signal separation, Computer science, Convergence (economics), Component (thermodynamics), Algorithm, Basis (linear algebra)

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