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...
- 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