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
| Summary: | 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 approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems. |
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