An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking
As the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the c...
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doaj-37f330edef59444d8a70554357aadab12021-06-01T01:11:54ZengMDPI AGAlgorithms1999-48932021-05-011416516510.3390/a14060165An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic MaskingShiyu Guo0Mengna Shi1Yanqi Zhou2Jiayin Yu3Erfu Wang4Electrical Engineering College, Heilongjiang University, Harbin 150080, ChinaElectrical Engineering College, Heilongjiang University, Harbin 150080, ChinaElectrical Engineering College, Heilongjiang University, Harbin 150080, ChinaElectrical Engineering College, Heilongjiang University, Harbin 150080, ChinaElectrical Engineering College, Heilongjiang University, Harbin 150080, ChinaAs the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the convolutional signal are crucial steps in obtaining source information. In this paper, chaotic masking technology is used to guarantee the transmission safety of speech signals, and a fast fixed-point independent vector analysis algorithm is used to solve the problem of convolutional blind source separation. First, the chaotic masking is performed before the speech signal is sent, and the convolutional mixing process of multiple signals is simulated by impulse response filter. Then, the observed signal is transformed to the frequency domain by short-time Fourier transform, and instantaneous blind source separation is performed using a fast fixed-point independent vector analysis algorithm. The algorithm can preserve the high-order statistical correlation between frequencies to solve the permutation ambiguity problem in independent component analysis. Simulation experiments show that this algorithm can efficiently complete the blind extraction of convolutional signals, and the quality of recovered speech signals is better. It provides a solution for the secure transmission and effective separation of speech signals in multipath transmission channels.https://www.mdpi.com/1999-4893/14/6/165independent vector analysisconvolutional blind source separationmultipath transmissionchaotic masking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shiyu Guo Mengna Shi Yanqi Zhou Jiayin Yu Erfu Wang |
spellingShingle |
Shiyu Guo Mengna Shi Yanqi Zhou Jiayin Yu Erfu Wang An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking Algorithms independent vector analysis convolutional blind source separation multipath transmission chaotic masking |
author_facet |
Shiyu Guo Mengna Shi Yanqi Zhou Jiayin Yu Erfu Wang |
author_sort |
Shiyu Guo |
title |
An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking |
title_short |
An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking |
title_full |
An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking |
title_fullStr |
An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking |
title_full_unstemmed |
An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking |
title_sort |
efficient convolutional blind source separation algorithm for speech signals under chaotic masking |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2021-05-01 |
description |
As the main method of information transmission, it is particularly important to ensure the security of speech communication. Considering the more complex multipath channel transmission situation in the wireless communication of speech signals and separating or extracting the source signal from the convolutional signal are crucial steps in obtaining source information. In this paper, chaotic masking technology is used to guarantee the transmission safety of speech signals, and a fast fixed-point independent vector analysis algorithm is used to solve the problem of convolutional blind source separation. First, the chaotic masking is performed before the speech signal is sent, and the convolutional mixing process of multiple signals is simulated by impulse response filter. Then, the observed signal is transformed to the frequency domain by short-time Fourier transform, and instantaneous blind source separation is performed using a fast fixed-point independent vector analysis algorithm. The algorithm can preserve the high-order statistical correlation between frequencies to solve the permutation ambiguity problem in independent component analysis. Simulation experiments show that this algorithm can efficiently complete the blind extraction of convolutional signals, and the quality of recovered speech signals is better. It provides a solution for the secure transmission and effective separation of speech signals in multipath transmission channels. |
topic |
independent vector analysis convolutional blind source separation multipath transmission chaotic masking |
url |
https://www.mdpi.com/1999-4893/14/6/165 |
work_keys_str_mv |
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