Blind source separation of mixtures of speech signals with unknown propagation delays

2000 ◽  
Vol 108 (5) ◽  
pp. 2629-2629 ◽  
Author(s):  
Phillip De Leon ◽  
Yunsheng Ma
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Speech Signals ◽  
Propagation Delays ◽  
Separation Of Mixtures

scholarly journals An Efficient Convolutional Blind Source Separation Algorithm for Speech Signals under Chaotic Masking

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 165
Author(s):  
Shiyu Guo ◽  
Mengna Shi ◽  
Yanqi Zhou ◽  
Jiayin Yu ◽  
Erfu Wang
Keyword(s):  
Fixed Point ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vector Analysis ◽  
Speech Signals ◽  
Speech Communication ◽  
Source Information ◽  
Independent Vector ◽  
Analysis Algorithm ◽  
Independent Vector Analysis

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.

Real-Time Blind Source Separation for Moving Speech Signals

2005 ◽  
pp. 353-369 ◽  
Author(s):  
Ryo Mukai ◽  
Hiroshi Sawada ◽  
Shoko Araki ◽  
Shoji Makino
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Source Separation ◽  
Speech Signals
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