scholarly journals Real Time Blind Source Separation in Reverberant Environments

2021 ◽  
Author(s):  
◽  
Timothy Sherry
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real World Data ◽  
The Core ◽  
Reverberant Environments ◽  
Ica Algorithm ◽  
Stationary Sources ◽  
Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

scholarly journals Real Time Blind Source Separation in Reverberant Environments

2021 ◽  
Author(s):  
◽  
Timothy Sherry
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real World Data ◽  
The Core ◽  
Reverberant Environments ◽  
Ica Algorithm ◽  
Stationary Sources ◽  
Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

Convolutive Blind Source Separation Based on Wavelet De-Noising

2013 ◽  
Vol 756-759 ◽  
pp. 3356-3361 ◽  
Author(s):  
Hong Bin Zhang ◽  
Peng Fei Xu
Keyword(s):  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Noise Pollution ◽  
Processing Method ◽  
Separation System ◽  
Communication Signals ◽  
The Time Domain ◽  
Ica Algorithm ◽  
Convolutive Blind Source Separation

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

scholarly journals On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures

2021 ◽  
pp. 1-36
Author(s):  
Takuya Isomura ◽  
Taro Toyoizumi
Keyword(s):  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Zero Element ◽  
Data Availability ◽  
Real World Data ◽  
Nonlinear Source ◽  
World Data

For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately—when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality. Our proposed theorem, termed the asymptotic linearization theorem, theoretically guarantees that applying linear PCA to the inputs can reliably extract a subspace spanned by the linear projections from every hidden source as the major components—and thus projecting the inputs onto their major eigenspace can effectively recover a linear transformation of the hidden sources. Then subsequent application of linear ICA can separate all the true independent hidden sources accurately. Zero-element-wise-error nonlinear BSS is asymptotically attained when the source dimensionality is large and the input dimensionality is sufficiently larger than the source dimensionality. Our proposed “Data Availability” section just before the Acknowledgments is validated analytically and numerically. Moreover, the same computation can be performed by using Hebbian-like plasticity rules, implying the biological plausibility of this nonlinear BSS strategy. Our results highlight the utility of linear PCA and ICA for accurately and reliably recovering nonlinearly mixed sources and suggest the importance of employing sensors with sufficient dimensionality to identify true hidden sources of real-world data.

scholarly journals A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

2017 ◽  
Vol 7 (4) ◽  
pp. 1906 ◽  
Author(s):  
Abouzid Houda ◽  
Chakkor Otman
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real Life ◽  
Signal Separation ◽  
Mixing Process ◽  
The Real ◽  
Input Signals ◽  
Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the  nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation.  But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

Sign in / Sign up

Export Citation Format

Share Document