Bayesian Unsupervised Learning for Source Separation with Mixture of Gaussians Prior

被引：0
作者：
Hichem Snoussi
Ali Mohammad-Djafari
机构：
[1] Laboratoire des Signaux et Systèmes (CNRS,
[2] SUPÉLEC,undefined
[3] UPS),undefined
[4] SUPÉLEC,undefined
来源：
Journal of VLSI signal processing systems for signal, image and video technology | 2004年 / 37卷
关键词：
source separation; HMM models; EM algorithm; Gibbs sampling;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as i.i.d hidden variables. We extend this modelization to incorporate a Markovian structure for the labels. This extension is important for practical applications which are abundant: unsupervised classification and segmentation, pattern recognition and speech signal processing.
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页码：263 / 279
页数：16
相关论文
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