Hidden Markov modeling of noise periodograms using Rayleigh mixture models

被引：0
|
作者：
Sorensen, Karsten Vandborg [1 ]
Andersen, Soren Vang [1 ]
机构：
[1] Aalborg Univ, Dept Commun Technol, DK-9220 Aalborg O, Denmark
来源：
2005 39TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, VOLS 1 AND 2 | 2005年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we derive an Expectation-Maximization algorithm for hidden Markov models (HMMs) with a multivariate Rayleigh mixture model (RMM) in each state. We compare the use of multivariate RMMs to multivariate Gaussian mixture models in the general case where the HMM is a dynamic model and for the special case where it has a single state and reduces to a static model. We evaluate the proposed method when used to model probability density of periodpgrams from real-life noise sources and white Gaussian noise, which we include for reference purposes.
引用
收藏
页码：1666 / 1670
页数：5
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