GA_FastICA Algorithmfor Speech Separation

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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Bibliographic Details
Main Authors: LAN Chao-feng, CHEN Ying-qi, LIN Xiao-jia, LIU Yan, CHEN Xu-qi
Format: Article
Language:zho
Published: Harbin University of Science and Technology Publications 2022-12-01
Series:Journal of Harbin University of Science and Technology
Subjects:
speech processing
speech separation
independent component analysis
signal to noise ratio
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2161
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Summary: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.
ISSN:1007-2683

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