2009•Jisuanji fangzhenRequires access

An Adaptive Genetic Algorithm for Blind Source Separation

Huang Shuang-feng

Abstract

BSS(Blind Source Separation)and ICA(Independent Component Analysis),which do not require priorknowledge of signals,are widely applied.Independent component analysis(ICA) is a new technique in signal processing.The goal of ICA is to find a separation matrix so that each component of the output signal obtained by transforming the observed multidimensional mixture of data is independent.An adaptive genetic algorithm which combines GA with ICA for blind source separation is proposed in this paper.The convergence and stability of the algorithm are confirmed by comparing with traditional GA.Three sound signals are tested and their validity is confirmed by signal separation test.

About this research paper

What this paper is about

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Key concepts: Independent component analysis, Blind signal separation, Source separation, SIGNAL (programming language), Algorithm, Computer science, Component (thermodynamics), Convergence (economics)