Implementation of Pipelined FastICA on FPGA for Real-Time Blind Source Separation

2008 ◽  
Vol 19 (6) ◽  
pp. 958-970 ◽  
Author(s):  
Kuo-Kai Shyu ◽  
Ming-Huan Lee ◽  
Yu-Te Wu ◽  
Po-Lei Lee
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Source Separation

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>

Development and evaluation of pocket-size real-time blind source separation microphone

2009 ◽  
Vol 30 (4) ◽  
pp. 297-304 ◽  
Author(s):  
Takashi Hiekata ◽  
Yohei Ikeda ◽  
Toshiro Yamashita ◽  
Takashi Morita ◽  
Ruoyu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Source Separation

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

2004 ◽  
Vol 04 (02) ◽  
pp. 193-206 ◽  
Author(s):  
SHUXUE DING ◽  
JIE HUANG ◽  
DAMING WEI
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Lower Cost ◽  
Source Separation ◽  
Least Square ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Recursive Least Square ◽  
Second Order Statistics

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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