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

页码：9 / 12

页数：4

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