Automatic Diagnosis of Snoring Sounds with the Developed Artificial Intelligence-based Hybrid Model
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Turkish Journal of Science and Technology
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Sleep patterns and sleep continuity have a great impact on people's quality of life. The sound of snoring both reduces the sleep quality of the snorer and disturbs other people in the environment. Interpretation of sleep signals by experts and diagnosis of the disease is a difficult and costly process. Therefore, in the study, an artificial intelligence-based hybrid model was developed for the classification of snoring sounds. In the proposed method, first of all, sound signals were converted into images using the Mel-spectrogram method. The feature maps of the obtained images were obtained using Alexnet and Resnet101 architectures. After combining the feature maps that are different in each architecture, dimension reduction was made using the NCA dimension reduction method. The feature map optimized using the NCA method was classified in the Bilayered Neural Network. In addition, spectrogram images were classified with 8 different CNN models to compare the performance of the proposed model. Later, in order to test the performance of the proposed model, feature maps were obtained using the MFCC method and the obtained feature maps were classified in different classifiers. The accuracy value obtained in the proposed model is 99.5%.
Açıklama
Anahtar Kelimeler
Deep Learning, Classifiers, CNN, MFCC, Snoring, Spectrogram
Kaynak
Turkish Journal of Science and Technology
WoS Q Değeri
Scopus Q Değeri
Cilt
17
Sayı
2
Künye
M. YILDIRIM, “Automatic Diagnosis of Snoring Sounds with the Developed Artificial Intelligence-based Hybrid Model,” Turkish Journal of Science and Technology, vol. 17, no. 2, pp. 405–416, Sep. 2022.
