scholarly journals Correlation matrix estimation by an optimally controlled recursive average method and its application to blind source separation

2010 ◽  
Vol 31 (3) ◽  
pp. 205-212 ◽  
Author(s):  
Hirofumi Nakajima ◽  
Kazuhiro Nakadai ◽  
Yuji Hasegawa ◽  
Hiroshi Tsujino
Keyword(s):  
Blind Source Separation ◽  
Correlation Matrix ◽  
Average Method ◽  
Source Separation ◽  
Matrix Estimation

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.

Modal Identification Using Blind Source Separation Based on Moving Average Method

2013 ◽  
Vol 378 ◽  
pp. 375-381
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Blind Source Separation ◽  
Average Method ◽  
Moving Average ◽  
Source Separation ◽  
Modal Identification ◽  
Separation Performance ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
The Stability

The paper discusses the basic principles of blind source separation and modal identification of structures, analyses the feasibility that using blind source separation techniques for modal parameter identification. According to the noisy features of the measured data in experiments, a second-order blind identification algorithm based on moving average method is proposed. By moving average method the noises are efficiently eliminated. It greatly improves the separation performance of this algorithm. The cantilever experiments verify the stability and the applicability of the algorithm.

scholarly journals Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangdong Huang ◽  
Xukang Jin ◽  
Haipeng Fu
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Low Complexity ◽  
Fast Response ◽  
Rotating Machinery ◽  
Matrix Estimation ◽  
Application Fields ◽  
Spectrum Correction ◽  
Machinery Fault Diagnosis

Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modifiedk-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields.

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