Semi-blind source separation using binary masking and independent vector analysis

2014 ◽  
Vol 10 (1) ◽  
pp. 114-115 ◽  
Author(s):  
Yuuki Tachioka ◽  
Tomohiro Narita ◽  
Jun Ishii
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Vector Analysis ◽  
Independent Vector ◽  
Independent Vector Analysis ◽  
Binary Masking

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.

scholarly journals Flow-Based Independent Vector Analysis for Blind Source Separation

2020 ◽  
Vol 27 ◽  
pp. 2173-2177
Author(s):  
Aditya Arie Nugraha ◽  
Kouhei Sekiguchi ◽  
Mathieu Fontaine ◽  
Yoshiaki Bando ◽  
Kazuyoshi Yoshii
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Vector Analysis ◽  
Independent Vector ◽  
Independent Vector Analysis

scholarly journals Decoupled Independent Vector Analysis Algorithm for Convolutive Blind Source Separation without Orthogonality Constraint on the Demixing Matrices

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Pengfei Wang ◽  
Jiong Li ◽  
Hang Zhang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Vector Analysis ◽  
Statistical Dependence ◽  
Separation Performance ◽  
Independent Vector ◽  
Independent Vector Analysis ◽  
Orthogonality Constraint ◽  
Error Accumulation ◽  
Convolutive Blind Source Separation

In this paper, we consider the problem of convolutive blind source separation in frequency domain and introduce a solution to the problem in an independent vector analysis (IVA) framework. IVA utilizes both the statistical independence of different sources in each frequency bin and the statistical dependence of the same source in different frequency bins. However, most of previous works impose orthogonality constraint on the rows of each separation matrix which may undermine the separation performance. In this work, we propose a nonorthogonal IVA algorithm based on decoupled relative Newton method. This proposed algorithm updates the separation matrices row by row, and unlike deflation separation algorithm, there is no separation error accumulation arising. Simulation results are provided to show the superior convergence behavior and separation performance of the proposed algorithm.

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