Artificial Neural Networks for Acoustic Lung Signals Classification

被引：0

作者：

Orjuela-Canon, Alvaro D. ^{[1
]}

Gomez-Cajas, Diego F. ^{[1
]}

Jimenez-Moreno, Robinson ^{[2
]}

机构：

[1] Univ Antonio Narino, Elect & Biomed Fac, GIBIO, Bogota, Colombia

[2] Univ Antonio Narino, GRITEL, Dept Elect Engn, Bogota, Colombia

来源：

PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014 | 2014年 / 8827卷

关键词：

Artificial Neural Networks; Multilayer Perceptron; Mel Frequency Cepstral Coefficients; Acoustic Lung Signals; SOUNDS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A classification of acoustic lung signals for the respiratory disease diagnosis problem is studied in the present work. Models based on artificial neural networks, using Mel Frequency Cepstral Coefficients for training are employed in this task. Results show that neural networks are comparable, and in some cases better, with other classification techniques as Gaussian Mixture Models, that work on the same database.

引用

页码：214 / 221

页数：8

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