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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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/118 |
| _version_ | 1797497669047812096 |
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| author | Jiali Zi Danju Lv Jiang Liu Xin Huang Wang Yao Mingyuan Gao Rui Xi Yan Zhang |
| author_facet | Jiali Zi Danju Lv Jiang Liu Xin Huang Wang Yao Mingyuan Gao Rui Xi Yan Zhang |
| author_sort | Jiali Zi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:22:32Z |
| format | Article |
| id | doaj.art-da9bc2bcd7d247ca985cb4fbc4467d26 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:22:32Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-da9bc2bcd7d247ca985cb4fbc4467d262023-11-23T12:17:08ZengMDPI AGSensors1424-82202021-12-0122111810.3390/s22010118Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-CorrelationJiali Zi0Danju Lv1Jiang Liu2Xin Huang3Wang Yao4Mingyuan Gao5Rui Xi6Yan Zhang7College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaSchool of Mathematics and Physics, Southwest Forestry University, Kunming 650224, ChinaIn 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.https://www.mdpi.com/1424-8220/22/1/118speech separationcross-correlationblind source separationswarm intelligence optimization algorithms |
| spellingShingle | Jiali Zi Danju Lv Jiang Liu Xin Huang Wang Yao Mingyuan Gao Rui Xi Yan Zhang Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation Sensors speech separation cross-correlation blind source separation swarm intelligence optimization algorithms |
| title | Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation |
| title_full | Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation |
| title_fullStr | Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation |
| title_full_unstemmed | Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation |
| title_short | Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation |
| title_sort | improved swarm intelligent blind source separation based on signal cross correlation |
| topic | speech separation cross-correlation blind source separation swarm intelligence optimization algorithms |
| url | https://www.mdpi.com/1424-8220/22/1/118 |
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