Respiratory sounds classification using Cepstral analysis and Gaussian Mixture Models

被引：0
|
作者：
Bahoura, M [1 ]
Pelletier, C [1 ]
机构：
[1] Univ Quebec, DMIG, Rimouski, PQ G5L 3A1, Canada
来源：
PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 | 2004年 / 26卷
关键词：
respiratory sounds; wheezes; cepstral analysis; Gaussian mixture models;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The Cepstral analysis is proposed with Gaussian Mixture Models (GMM) method to classify respiratory sounds in two categories: normal and wheezing. The sound signal is divided in overlapped segments, which are characterized by a reduced dimension feature vectors using Mel-Frequency Cepstral Coefficients (MFCC) or Subband based Cepstral parameters (SBC). The proposed schema is compared with other classifiers: Vector Quantization (VQ) and Multi-Layer Perceptron (MLP) neural networks. A post processing is proposed to improve the classification results.
引用
收藏
页码：9 / 12
页数：4
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