Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. Results showed that with a supervised training, the classification reached rates of 85% in accuracy. Both approaches were used in this work to compare the utility of this tool in lung signals studies. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps. ORJUELA-CANON, Álvaro David and POSADA-QUINTERO, Hugo Fernando.
