.png){kind=icon}
.png){kind=icon}
Return
Source distribution models for blind source separation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2004.01.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Various methods have been proposed to. separate mixtures of sub- and super-Gaussian signals. Effectiveness of a blind source separation algorithm depends upon the source distribution model used for deriving the weight update rule. Different hypothesized source distribution models are used for representing sub- and super-Gaussian sources. In this paper, a more justifiable approach is considered for characterizing the source distributions. For a given density model of super- or sub-Gaussian sources, a symmetric probability density function around a Gaussian distribution is used for representing sub- or super-Gaussian sources. Hence, corresponding to a long tail sharper density function for super-Gaussian signals, a short-tail flatter density function for sub-Gaussian signals is obtained. Optimization of the objective function leads to two different nonlinear functions. This approach leads to appropriate handling of both kinds of signals simultaneously. Simulations results with audio signals have been presented. (C) 2004 Published by Elsevier B.V.
Keywords:
blind source separation (BSS)
objective function
probability density function (pdf)
sub-Gaussian signals
super-Gaussian signals
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available