Blind Separation of Noisy Mixed Speech Signals Based Independent Component Analysis

2010 ◽  
Author(s):  
Li Hongyan ◽  
Ren Guanglong
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Speech Signals ◽  
Blind Separation

scholarly journals Blind Separation of Positive Sources by Globally Convergent Gradient Search

2004 ◽  
Vol 16 (9) ◽  
pp. 1811-1825 ◽  
Author(s):  
Erkki Oja ◽  
Mark Plumbley
Keyword(s):  
Independent Component Analysis ◽  
Cost Function ◽  
Gradient Flow ◽  
Component Analysis ◽  
Independent Component ◽  
Gradient Algorithm ◽  
Stiefel Manifold ◽  
Blind Separation ◽  
Solution Methods ◽  
Simplified Solution

The instantaneous noise-free linear mixing model in independent component analysis is largely a solved problem under the usual assumption of independent nongaussian sources and full column rank mixing matrix. However, with some prior information on the sources, like positivity, new analysis and perhaps simplified solution methods may yet become possible. In this letter, we consider the task of independent component analysis when the independent sources are known to be nonnegative and well grounded, which means that they have a nonzero pdf in the region of zero. It can be shown that in this case, the solution method is basically very simple: an orthogonal rotation of the whitened observation vector into nonnegative outputs will give a positive permutation of the original sources. We propose a cost function whose minimum coincides with nonnegativity and derive the gradient algorithm under the whitening constraint, under which the separating matrix is orthogonal. We further prove that in the Stiefel manifold of orthogonal matrices, the cost function is a Lyapunov function for the matrix gradient flow, implying global convergence. Thus, this algorithm is guaranteed to find the nonnegative well-grounded independent sources. The analysis is complemented by a numerical simulation, which illustrates the algorithm.

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

Image Blind Separation Based on Adaptive Multi-Resolution Independent Component Analysis

2012 ◽  
Vol 586 ◽  
pp. 365-369
Author(s):  
Jing Hui Wang ◽  
Shu Gang Tang
Keyword(s):  
Independent Component Analysis ◽  
Quadratic Function ◽  
Component Analysis ◽  
Independent Component ◽  
Frequency Resolution ◽  
Frequency Function ◽  
Experimental Result ◽  
Blind Separation ◽  
Time Frequency ◽  
Dimensional Domain

In this paper, a novel image blind separation using adaptive multi-resolution independent component analysis is presented.This method separates mixed images based on quadratic function. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. According to the signal characteristics, we can choose the frequency resolution or scale resolution. The argorithm extends the separate technology from one dimensional domain to two dimensional domain,and it’s implement by adaptive procedure. The experimental result showed the method can be effective separation of mixed images. And it shows that the method is feasible.

AN EXPERIMENTAL COMPARISON OF NEURAL ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS AND BLIND SEPARATION

1999 ◽  
Vol 09 (02) ◽  
pp. 99-114 ◽  
Author(s):  
XAVIER GIANNAKOPOULOS ◽  
JUHA KARHUNEN ◽  
ERKKI OJA
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Experimental Comparison ◽  
Data Sets ◽  
Blind Separation ◽  
Real World Data ◽  
Computational Load

In this paper, we compare the performance of five prominent neural or adaptive algorithms designed for Independent Component Analysis (ICA) and blind source separation (BSS). In the first part of the study, we use artificial data for comparing the accuracy, convergence speed, computational load, and other relevant properties of the algorithms. In the second part, the algorithms are applied to three different real-world data sets. The task is either blind source separation or finding interesting directions in the data for visualisation purposes. We develop criteria for selecting the most meaningful basis vectors of ICA and measuring the quality of the results. The comparison reveals characteristic differences between the studied ICA algorithms. The most important conclusions of our comparison are robustness of the ICA algorithms with respect to modest modeling imperfections, and the superiority of fixed-point algorithms with respect to the computational load.

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