Blind Source Separation with Multi-Objective Optimization for Denoising

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Elektronika ir Elektrotechnika, vol.28, no.5, pp.62-67, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.5755/j02.eie.31232
  • Journal Name: Elektronika ir Elektrotechnika
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS), Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Page Numbers: pp.62-67
  • Keywords: Blind source separation, Denoising, Multiobjective optimization, Strength Pareto evolutionary algorithm 2, Optimization, INDEPENDENT COMPONENT ANALYSIS, FRAMEWORK, NOISE
  • Kayseri University Affiliated: No


© 2022 Authors. All rights reserved.Blind Source Separation is an optimization method frequently used in statistical signal processing applications. There are many application areas such as ambient listening, denoising, signal detection, and so on. In this study, a new Strength Pareto Evolutionary Algorithm 2-based signal separation method is proposed, which combines Multi-Objective Optimization and Blind Source Separation algorithms. The proposed method has been tested for denoising, which is widely used in biomedical signal processing. That is, the Electrocardiogram (ECG) and White Gaussian Noise are mixed together with normally distributed random numbers and the original signals of the mixed signals are obtained again. To evaluate the performance of the proposed method and others (Multi-Objective Blind Source Separation and Independent Component Analysis), the Signal-to-Noise Ratio (SNR) of the ECG signal obtained from mixed signals has been measured. As a result of the simulation studies, it is seen that the performance of the proposed method is satisfactory.