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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Autores:
Tipo de recurso:
Fecha de publicación:
2016
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/14156
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5300
https://repositorio.uptc.edu.co/handle/001/14156
Palabra clave:
acoustic lung signals
computer-aided decision making
self-organizing maps
mapas auto-organizados
señales acústicas de pulmón
sistemas de apoyo a decisión
Rights
License
http://purl.org/coar/access_right/c_abf218