WHEEZING SOUNDS DETECTION USING MULTIVARIATE GENERALIZED GAUSSIAN DISTRIBUTIONS

被引：17
|
作者：
Le Cam, S. [1 ]
Belghith, A. [1 ]
Collet, Ch. [1 ]
Salzenstein, F. [2 ]
机构：
[1] Univ Strasbourg, ULP, CNRS, LSIIT,UMR 7005, F-67070 Strasbourg, France
[2] Univ Strasbourg, ULP, CNRS, Lab INESS ,UMR 7163, F-67070 Strasbourg, France
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年
关键词：
Adventitious Respiratory Sounds; Data Fusion; Hidden Markov Chain; Generalized Gaussian Distribution; Copulas Theory; HIDDEN MARKOV-MODELS; SIGNAL;
D O I：
10.1109/ICASSP.2009.4959640
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
A wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing, commonly experienced by persons suffering from asthma. In this paper, we present a new method for the detection of wheezing sounds in the normal breathing sounds. In our study we perform an accurate statistical analysis of breathing signals. We suggest a modeling for wheezing and normal sounds in the wavelet packet domain using generalized gaussian distributions. Our detection method is based on a specific multimodal Markovian modeling proposed in a bayesian framework. We cope with the multidimensional aspect of the generalized gaussian distribution by using the theory of copulas. Experimental results are given in detail in this paper.
引用
收藏
页码：541 / +
页数：2
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