Authors:
A. A. Saraiva
1
;
2
;
D. B. S. B. S. Santos
3
;
A. A. Francisco
3
;
Jose Vigno Moura Sousa
4
;
3
;
N. M. Fonseca Ferreira
5
;
6
;
Salviano Soares
1
and
Antonio Valente
7
;
1
Affiliations:
1
University of Trás-os-Montes and Alto Douro,Vila Real, Portugal
;
2
University of Sao Paulo, Sao Carlos, Brazil
;
3
UESPI-University of State Piaui, Piripiri, Brazil
;
4
University Brazil, Sao Paulo, Brazil
;
5
Coimbra Polytechnic - ISEC, Coimbra, Portugal
;
6
Knowledge Engineering and Decision-Support Research Center (GECAD) of the Institute of Engineering, Polytechnic Institute of Porto, Porto, Portugal
;
7
INESC-TEC Technology and Science, Porto, Portugal
Keyword(s):
CNN, Sounds, Breath, MFCC.
Abstract:
Noting recent advances in the field of image classification, where convolutional neural networks (CNNs) are used to classify images with high precision. This paper proposes a method of classifying breathing sounds using CNN, where it is trained and tested. To do this, a visual representation of each audio sample was made that allows identifying resources for classification, using the same techniques used to classify images with high precision.For this we used the technique known as Mel Frequency Cepstral Coefficients (MFCCs). For each audio file in the dataset, we extracted resources with MFCC which means we have an image representation for each audio sample. The method proposed in this article obtained results above 74%, in the classification of respiratory sounds used in the four classes available in the database used (Normal, crackles, wheezes, Both).