Open Access
Issue
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
Article Number 01010
Number of page(s) 6
Section Artificial Intelligence, Machine Learning & Data Analytics
DOI https://doi.org/10.1051/e3sconf/202672301010
Published online 08 July 2026
  1. World Health Organization, Chronic resp i ra­ tory dise ases, https://www.who.int/health-to-pics/chronic-respiratory-diseases (2023), accessed: 2024-04-16 [Google Scholar]
  2. R.X.A. Pramono, S. Bowyer, E. Rodriguez-Villegas, Automatic adventitious respiratory sound analysis: A systemat ic review, PLOS ONE 12, e0l 77926 (2017). 10.1371/journal.pone.0l77926 [Google Scholar]
  3. A.R.A. Sovij arvi, F. Dalmasso, J. Vanderschoot, L.P. Malmberg, G. Righini, M. Rossi, Standardization of lung sound nomenclature, European Respiratory Re­ view 10, 434 (2000). [Google Scholar]
  4. M. Sarkar, I. Madabhavi, N. Niranjan, M. Dogra, Auscu ltatio n of the respiratory system, An nals of Thorac ic Medic i ne 10, 158 (2015). 10.4103/1817-1737.160839 [Google Scholar]
  5. A. Gurung, C.G. Scrafford, J.M. Tielsch, O.S. Levine, W. Checkley, Comp uterized lung sound analysis as diagnostic aid for the detection of ab­ normal lung sounds: A systematic review and meta­ analysis, Resp iratory Medicine 105, 1396 (2011). 10.1016/j.rmed.2011.05.007 [Google Scholar]
  6. E.D. McCollum et al., Digi tal ausc ultat io n in pediatric pne umo nia: progress and opportun i­ ties, The Lancet Digital Health 1, e421 (2019). 10.1016/S2589-7500(19)30154-1 [Google Scholar]
  7. G. Cbambres, P. Hanna, M. Desainte-Catherine, Au­ tomatic Detection of Patient with Respiratory Dis­ eases Using Lung Sound Analysis, in 2018 In­ ternational Coriference on Content-Based Multime­ dia Indexing (CBMI) (2018), pp. 1–6, https://ieeexplore.ieee.org/document/8516481 [Google Scholar]
  8. S. Gaim la, F. Tom, N. Kwatra, M. Jai n, RespireNet: A Deep Neural Network for Acc urately Detecting Abnormal Lung Sounds in Limited Data Set­ ting, in 2021 43rd Annual International Confer­ ence of the IEEE Engineering in M edicine & Biol­ og y Society (EMBC) (2021), pp. 527–530, https://ieeexplore.ieee.org/document/963@653 [Google Scholar]
  9. G. Petmezas, G.A. Cheimariotis, L. Ste fanopoulos, B. Rocha, R.P. Paiva, A.K. Katsaggelo s, N. Maglaveras, Automated lun g sound classific ation using a hy­ brid CNN-LSTM ne twork and focal loss function, Sensors 2 2, 1232 (2022). 100.3390/s22031 232 [Google Scholar]
  10. S. Kulka rni, A. Guntoro, F. Al-Turjman, Self - Supervised Audio Encoder wi th Contrastive Pre­ training for Respiratory Anomaly Detection, in 2023 IEEE Inte rnational Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023), pp. 1–5, https://ieeexplore.ieee.org/document/1@@95163 [Google Scholar]
  11. F. Demir, A.M. Ismael, A. Sengur, Classification of lun g sounds with CNN model using paralle l pooling structure, IEEE Access 8, 105376 (2020). 10.1109/ACCESS.2020.300011l [Google Scholar]
  12. R. Khan, S.U. Khan, U. Saeed, l.S. Koo, Auscultati on-b ased pulm onary dise ase detection through parallel transformation and deep learning, Bioengine ering 11, 586 (2024). 10.3390/bioengi­neeringl1060586 [Google Scholar]
  13. B.M. Rocha, D. Filas, L. Mend es, I. Vogiatzis, E. Peranton i, E. Kaimalis, P. Natsiavas, A. Oliveira, C. Jaccard, N. Martins et al., An Open Ac­ cess Database for the Eval uation of Respiratory Sound Classificati on Algorithms, in 2017 IEEE Life Sciences Conference (LSC) (2017), pp. 196--201, https://ieeexplore.ieee.org/document/8268165 [Google Scholar]
  14. N. Jaitly, G.E. Hinton, Vocal Tract Length Pertur­ bation (VTLP) Improves Speech Recog nition, in Proc. ICML Workshop on Deep Leam ing for Audio, Speech and Language (2013), https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41183.pdf [Google Scholar]
  15. D.S. Park, W. Chan, Y. Zhang, C.C. Chiu, B. Zoph, E.D. Cubu k, Q.V. Le, SpecAugment: A Simple Data Au gmentation Method for Automatic Speech Recognition, in Proc. lnterspeech 2019 (2019), pp. 2613–2617, https://www.isca-archive.org/interspeech_2@19/parkl9_interspeech.html [Google Scholar]
  16. S.S. Stevens, J. Vol kmann, E.B. Newma n, A scale for the meas urement of the psychological magnitude pitch, The Journal of the Acoustical Society of America 8, 185 (1937). 10.11 21/1.1915893 [Google Scholar]
  17. S. Monda] et al., Mel-frequency spectral features for objective assessme nt of resp iratory sounds, Biomedical Signal Processing and Control 71, 103230 (2022). 10.1016/j.bspc.2021.103230 [Google Scholar]
  18. S. Davis, P. Mermelstein, Comparison of parametric rep rese ntatio ns fo r monosyllabic word reco gnition in cont i nuous l y spoke n sentence s, IEEE Transactio ns on Aco us tics, Speech, and Signal Processing 28, 357 (1980). 10.1109/fASSP.1080.1163420 [Google Scholar]
  19. M. Bahoura, Pattern classifier for the de­ tection of wheezes and crackles, Comput­ ers in Biology and Medicine 39, 25 (2009). 10.1016/j.compbiomed.2008.11.001 [Google Scholar]
  20. J.C. Brown, Calculation of a constant Q spectr al transform, The Journa l of the Acoustical Society of America 89, 4 25 (1991). 10.1121/1.400476 [Google Scholar]
  21. J. Velayutham, S. Jayalakshmy, Mult i-resol ution analysis of respiratory sound s for pulmonary disorder detect ion, IEEE Sensors Journal 23, 17351 (2023). 10.1109/JSEN.2023.3283626 [Google Scholar]
  22. A. Roy, U. Satija, AsTFSONN: A Unified Frame­ work Based on Time-Frequency Domain Self­ Operational Neural etwork for Asthmatic Lung Sound Classific ation, in 2023 IEEE Int ernational Symposium on Medical Measurements and Ap­ plications (MeM eA) (2023), pp. 1–6, https://ieeexplore.ieee.org/document/1@171911 [Google Scholar]
  23. T.Y. Lin, P. Goyal, R. Gir shick, K. He, P. Dollar, Focal Loss for Dense Object Detection, in Procee d­ ings of the IEEE International Conference on Com­ puter Vision (!CCV ) (20 17), pp. 2980 - 2988, https://ieeexplore.ieee.org/document/8237586 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.