Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both...

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Detaylı Bibliyografya

Asıl Yazarlar:	Orjuela-Cañón, Álvaro David, Posada-Quintero, Hugo Fernando
Materyal Türü:	Online
Dil:	eng
Baskı/Yayın Bilgisi:	Universidad Pedagógica y Tecnológica de Colombia 2016
Konular:	acoustic lung signals computer-aided decision making self-organizing maps mapas auto-organizados señales acústicas de pulmón sistemas de apoyo a decisión
Online Erişim:	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5300