Research on blind source separation of marine mammals signal processing under watercraft emitted noise

2012 ◽  
Vol 131 (4) ◽  
pp. 3423-3423
Author(s):  
Zhang Liang ◽  
Guo LongXiang
Keyword(s):  
Signal Processing ◽  
Blind Source Separation ◽  
Marine Mammals ◽  
Source Separation

Applications to Signal Processing - Blind Source Separation

2012 ◽  
pp. 119-138
Author(s):  
Hime Aguiar e Oliveira Junior ◽  
Lester Ingber ◽  
Antonio Petraglia ◽  
Mariane Rembold Petraglia ◽  
Maria Augusta Soares Machado
Keyword(s):  
Signal Processing ◽  
Blind Source Separation ◽  
Source Separation

Robust Blind Source Separation by Beta Divergence

2002 ◽  
Vol 14 (8) ◽  
pp. 1859-1886 ◽  
Author(s):  
Minami Mihoko ◽  
Shinto Eguchi
Keyword(s):  
Signal Processing ◽  
Data Analysis ◽  
Blind Source Separation ◽  
Financial Market ◽  
Source Separation ◽  
Robust Method ◽  
Communication Engineering ◽  
Biological Signal Processing ◽  
Simulation Results ◽  
Linear Mixtures

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on theβ divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.

Blind Separability of Reverberation and Target Echo Based on Spatial Correlation

2011 ◽  
Vol 128-129 ◽  
pp. 538-543
Author(s):  
Xiu Kun Li ◽  
Qi Yong Wang
Keyword(s):  
Signal Processing ◽  
Spatial Correlation ◽  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Sea Bottom ◽  
Transmitted Signal ◽  
Signal Processing Methods ◽  
Nonstationary Source ◽  
Different Sources

The detection of buried targets in shallow water is a tough task in the presence of sea-bottom reverberation. Because both target echo and reverberation are caused by the transmitted signal, they are mixed together in both time domain and frequency domain, which makes traditional signal processing methods inefficient. Blind Source Separation (BSS) is expected to isolate the reverberation from the target echo. However, the feasibility should be proved before separation. In this paper, a method based on spatial correlation is proposed to determine whether reverberation and target echo can be separated as different sources. Then, considering the nonstationarity of the reverberation, SONS (Second Order Nonstationary Source Separation) is applied to separate the original received signals. The sea experiment result shows that BSS is not only feasible but also valid to separate target echo and reverberation, and the target echo after BSS is of higher SRR which makes further process more credible.

scholarly journals Graph Signal Processing Meets Blind Source Separation

2021 ◽  
pp. 1-1
Author(s):  
Jari Miettinen ◽  
Eyal Nitzan ◽  
Sergiy A. Vorobyov ◽  
Esa Ollila
Keyword(s):  
Signal Processing ◽  
Blind Source Separation ◽  
Source Separation ◽  
Graph Signal Processing

Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

2011 ◽  
Vol 42 (10) ◽  
pp. 55-61 ◽  
Author(s):  
Yu Jiang ◽  
Li Qin ◽  
Yuelei Zhang ◽  
Jingping Wu
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Vibration Signal ◽  
Normal Operation ◽  
Mode Decomposition ◽  
Gear Fault

Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.

Rolling Bearing Fault Diagnosis Based on Blind Source Separation

2012 ◽  
Vol 217-219 ◽  
pp. 2546-2549 ◽  
Author(s):  
Chang Zheng Chen ◽  
Qiang Meng ◽  
Hao Zhou ◽  
Yu Zhang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

This document presents fault diagnosis method of rolling bearing based on blind source separation. The algorithm based on fast ICA is improved to separate fault signals according to the rolling bearing’s fault characteristics. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

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