Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 1; s. 118
Hlavní autori: Zi, Jiali, Lv, Danju, Liu, Jiang, Huang, Xin, Yao, Wang, Gao, Mingyuan, Xi, Rui, Zhang, Yan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 24.12.2021
MDPI
Predmet:
Accuracy
Algorithms
Animals
blind source separation
Butterflies
cross-correlation
Fault diagnosis
Intelligence
Optimization algorithms
Signal processing
speech separation
swarm intelligence optimization algorithms
swarm intelligence optimization algorithms
cross-correlation
speech separation
blind source separation
ISSN:1424-8220, 1424-8220
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Shrnutí:In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22010118