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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 1; S. 118
Hauptverfasser: Zi, Jiali, Lv, Danju, Liu, Jiang, Huang, Xin, Yao, Wang, Gao, Mingyuan, Xi, Rui, Zhang, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 24.12.2021
MDPI
Schlagworte:
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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Abstract 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.
AbstractList 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.
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.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.
Author Huang, Xin
Liu, Jiang
Zi, Jiali
Gao, Mingyuan
Lv, Danju
Zhang, Yan
Xi, Rui
Yao, Wang
AuthorAffiliation 2 School of Mathematics and Physics, Southwest Forestry University, Kunming 650224, China; zhangyan@swfu.edu.cn
1 College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; zijiali@swfu.edu.cn (J.Z.); jungleliu@swfu.edu.cn (J.L.); huangxin615@swfu.edu.cn (X.H.); yaowang@swfu.edu.cn (W.Y.); TuAYuan@swfu.edu.cn (M.G.); xirui@swfu.edu.cn (R.X.)
AuthorAffiliation_xml – name: 1 College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China; zijiali@swfu.edu.cn (J.Z.); jungleliu@swfu.edu.cn (J.L.); huangxin615@swfu.edu.cn (X.H.); yaowang@swfu.edu.cn (W.Y.); TuAYuan@swfu.edu.cn (M.G.); xirui@swfu.edu.cn (R.X.)
– name: 2 School of Mathematics and Physics, Southwest Forestry University, Kunming 650224, China; zhangyan@swfu.edu.cn
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Issue 1
Keywords swarm intelligence optimization algorithms
cross-correlation
speech separation
blind source separation
Language English
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Snippet 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...
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StartPage 118
SubjectTerms Accuracy
Algorithms
Animals
blind source separation
Butterflies
cross-correlation
Fault diagnosis
Intelligence
Optimization algorithms
Signal processing
speech separation
swarm intelligence optimization algorithms
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Title Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation
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