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Blind Source Separation Based on FastICA

Yang Liu, Zhang Ming, Jiang Longbin

Open publisher page 6 citations

Abstract

Blind source separation has common considerable attention from the signal processing community and the neural network community. It is becoming a hot topic. The independent component analysis (ICA) is a new method of blind source separation which is developing in these years. In this paper,a fast algorithm of blind source separation based on ICA is introduced,the result of experiment show that Fast independent component analysis (FastICA) can separate every independent component effectively , It is more flexible and robust than those using the conventional independent component analysis methods.

About this research paper

What this paper is about

Blind source separation has common considerable attention from the signal processing community and the neural network community. It is becoming a hot topic. The independent component analysis (ICA) is a new method of blind source separation which is developing in these years. In this paper,a fast algorithm of blind source separation based on ICA is introduced,the result of experiment show that Fast independent component analysis (FastICA) can separate every independent component effectively , It is more flexible and robust than those using the conventional independent component analysis methods.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

Blind source separation has common considerable attention from the signal processing community and the neural network community. It is becoming a hot topic. The independent component analysis (ICA) is a new method of blind source separation which is developing in these years. In this paper,a fast algorithm of blind source separation based on ICA is introduced,the result of experiment show that Fast independent component analysis (FastICA) can separate every independent component effectively , It is more flexible and robust than those using the conventional independent component analysis methods.

Key concepts: Independent component analysis, FastICA, Blind signal separation, Computer science, Source separation, Component (thermodynamics), Separation (statistics), Signal processing

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