GA_FastICA Algorithmfor Speech Separation
With the development of speech processing technology, new speech separation algorithms are constantly proposed. The GA_FastICA algorithm is proposed by combining the Geometric Approach (GA) algorithm and Fast Independent Component Analysis (FastICA) algorithm for the problem of unsatisfactory separa...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2022-12-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2161 |
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| author | LAN Chao-feng CHEN Ying-qi LIN Xiao-jia LIU Yan CHEN Xu-qi |
| author_facet | LAN Chao-feng CHEN Ying-qi LIN Xiao-jia LIU Yan CHEN Xu-qi |
| author_sort | LAN Chao-feng |
| collection | DOAJ |
| description | With the development of speech processing technology, new speech separation algorithms are constantly proposed. The GA_FastICA algorithm is proposed by combining the Geometric Approach (GA) algorithm and Fast Independent Component Analysis (FastICA) algorithm for the problem of unsatisfactory separation due to the noise in the observed signal and combining the geometric operation method. The time domain waveforms of the separated speech signals are plotted, and the correlation coefficients of the original and separated speech signals are given to investigate the effectiveness of the GA algorithm.When the signal-to-noise ratio is 4dB, the correlation coefficient of the separated speech signal and the original speech signal is 0.7852 . The experimental simulation results show that under the signal-to-noise ratio of 12dB, factory and babble noise conditions, the GA_FastICA algorithm improves the correlation coefficient by 0.0212 and 0.0304 compared with the FastICA algorithm, and the correlation coefficients were improved by 0.1374 and 0.1328 for a signal-to-noise ratio of 8dB. The GA_FastICA algorithm can effectively separate the speech signal, and the noisy environment GA_FastICA algorithm can effectively separate speech signals and has a better speech separation effect. |
| format | Article |
| id | doaj-art-21009c98df1747f09d222c84a9c0e4fb |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-21009c98df1747f09d222c84a9c0e4fb2025-08-20T03:51:48ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-12-012706808710.15938/j.jhust.2022.06.010GA_FastICA Algorithmfor Speech SeparationLAN Chao-feng0CHEN Ying-qi1LIN Xiao-jia2LIU Yan3CHEN Xu-qi4School of Measurement and Communications Engineering, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Measurement and Communications Engineering, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Measurement and Communications Engineering, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Measurement and Communications Engineering, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Measurement and Communications Engineering, Harbin University of Science and Technology, Harbin 150080,ChinaWith the development of speech processing technology, new speech separation algorithms are constantly proposed. The GA_FastICA algorithm is proposed by combining the Geometric Approach (GA) algorithm and Fast Independent Component Analysis (FastICA) algorithm for the problem of unsatisfactory separation due to the noise in the observed signal and combining the geometric operation method. The time domain waveforms of the separated speech signals are plotted, and the correlation coefficients of the original and separated speech signals are given to investigate the effectiveness of the GA algorithm.When the signal-to-noise ratio is 4dB, the correlation coefficient of the separated speech signal and the original speech signal is 0.7852 . The experimental simulation results show that under the signal-to-noise ratio of 12dB, factory and babble noise conditions, the GA_FastICA algorithm improves the correlation coefficient by 0.0212 and 0.0304 compared with the FastICA algorithm, and the correlation coefficients were improved by 0.1374 and 0.1328 for a signal-to-noise ratio of 8dB. The GA_FastICA algorithm can effectively separate the speech signal, and the noisy environment GA_FastICA algorithm can effectively separate speech signals and has a better speech separation effect.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2161speech processingspeech separationindependent component analysissignal to noise ratio |
| spellingShingle | LAN Chao-feng CHEN Ying-qi LIN Xiao-jia LIU Yan CHEN Xu-qi GA_FastICA Algorithmfor Speech Separation Journal of Harbin University of Science and Technology speech processing speech separation independent component analysis signal to noise ratio |
| title | GA_FastICA Algorithmfor Speech Separation |
| title_full | GA_FastICA Algorithmfor Speech Separation |
| title_fullStr | GA_FastICA Algorithmfor Speech Separation |
| title_full_unstemmed | GA_FastICA Algorithmfor Speech Separation |
| title_short | GA_FastICA Algorithmfor Speech Separation |
| title_sort | ga fastica algorithmfor speech separation |
| topic | speech processing speech separation independent component analysis signal to noise ratio |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2161 |
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