Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67239, first published .
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Journals

  1. López-Canay J, Ramos-Hernández C, Casal-Guisande M, Botana-Rial M, Leiro-Fernández V, Fernández-Villar A. Design and Development of an Intelligent System Applied to the Interpretation of Lung Auscultation. IEEE Access 2025;13:176245 View
  2. Türe H, Aygün E. Solunum Sesi Sınıflandırması için Klasik ve Derin Öğrenme Modellerinin Karşılaştırılması. Karadeniz Fen Bilimleri Dergisi 2025;15(4):1668 View

Conference Proceedings

  1. Fatema K, Habib M. 2025 7th International Conference on Electrical Information and Communication Technology (EICT). Weighted Cross-Entropy Loss Based Respiratory Diseases Classification View
  2. Hirata K, Suzuki S, Kobayashi Y. 2025 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). Effects of Electronic Noise on Environmental Sound Classification Using Convolutional Neural Network with Bispectrograms and Its Improvement View