{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:20:26Z","timestamp":1783938026593,"version":"3.55.0"},"reference-count":100,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pfizer Center for Digital Innovation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice\/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases\u2019 symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.<\/jats:p>","DOI":"10.3390\/s24041173","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:50:27Z","timestamp":1707709827000},"page":"1173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review"],"prefix":"10.3390","volume":"24","author":[{"given":"Panagiotis","family":"Kapetanidis","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fotios","family":"Kalioras","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7016-7533","authenticated-orcid":false,"given":"Constantinos","family":"Tsakonas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0238-8243","authenticated-orcid":false,"given":"Pantelis","family":"Tzamalis","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Kontogiannis","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9766-983X","authenticated-orcid":false,"given":"Theodora","family":"Karamanidou","sequence":"additional","affiliation":[{"name":"Pfizer Center for Digital Innovation, 55535 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2389-4329","authenticated-orcid":false,"given":"Thanos G.","family":"Stavropoulos","sequence":"additional","affiliation":[{"name":"Pfizer Center for Digital Innovation, 55535 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3765-5636","authenticated-orcid":false,"given":"Sotiris","family":"Nikoletseas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3040","DOI":"10.1016\/j.procs.2021.09.076","article-title":"Non-invasive devices for respiratory sound monitoring","volume":"192","author":"Troncoso","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100832","DOI":"10.1016\/j.imu.2021.100832","article-title":"Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey","volume":"29","author":"Ijaz","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeon, J., Han, Y.J., Joo, Y., Lee, J., Lee, S., and Im, S. (2020). Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. J. Clin. Med., 9.","DOI":"10.3390\/jcm9113415"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"EL253","DOI":"10.1121\/10.0001933","article-title":"Asthmatic versus healthy child classification based on cough and vocalised \/a:\/ sounds","volume":"148","author":"Hee","year":"2020","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1038\/s41746-021-00472-x","article-title":"Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis","volume":"4","author":"Claxton","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_6","first-page":"34","article-title":"Robust Detection of COVID-19 in Cough Sounds","volume":"2","author":"Mouawad","year":"2021","journal-title":"Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"580080","DOI":"10.3389\/frobt.2021.580080","article-title":"Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network","volume":"8","author":"Zhou","year":"2021","journal-title":"Front. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100319","DOI":"10.1016\/j.imu.2020.100319","article-title":"Cough sound analysis and objective correlation with spirometry and clinical diagnosis","volume":"19","author":"Rudraraju","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/OJEMB.2020.3026928","article-title":"COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings","volume":"1","author":"Laguarta","year":"2020","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bansal, V., Pahwa, G., and Kannan, N. (2020, January 2\u20134). Cough Classification for COVID-19 based on audio mfcc features using Convolutional Neural Networks. Proceedings of the 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India.","DOI":"10.1109\/GUCON48875.2020.9231094"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Andreu-Perez, J., Espinosa, H.P., Timonet, E., Kiani, M., Gir\u00f3n-P\u00e9rez, M.I., Benitez-Trinidad, A.B., Jarchi, D., Rosales-P\u00e9rez, A., Gatzoulis, N., and Reyes-Galaviz, O.F. (2021). A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels. arXiv.","DOI":"10.31219\/osf.io\/tm2f7"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Feng, K., He, F., Steinmann, J., and Demirkiran, I. (2021, January 10\u201313). Deep-learning Based Approach to Identify COVID-19. Proceedings of the SoutheastCon 2021, Atlanta, GA, USA.","DOI":"10.1109\/SoutheastCon45413.2021.9401826"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hassan, A., Shahin, I., and Alsabek, M.B. (2020, January 3\u20135). COVID-19 Detection System using Recurrent Neural Networks. Proceedings of the 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Sharjah, United Arab Emirates.","DOI":"10.1109\/CCCI49893.2020.9256562"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.imu.2020.100378","article-title":"AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app","volume":"20","author":"Imran","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pal, A., and Sankarasubbu, M. (2020). Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing. arXiv.","DOI":"10.1145\/3412841.3441943"},{"key":"ref_16","unstructured":"Pahar, M., and Niesler, T. (2021). Machine Learning based COVID-19 Detection from Smartphone Recordings: Cough, Breath and Speech. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"You, M., Wang, W., Li, Y., Liu, J., Xu, X., and Qiu, Z. (2022). Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression. Biomed. Signal Process. Control., 72.","DOI":"10.1016\/j.bspc.2021.103304"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1002\/ppul.25801","article-title":"Development and technical validation of a smartphone-based pediatric cough detection algorithm","volume":"57","author":"Kruizinga","year":"2022","journal-title":"Pediatr. Pulmonol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chowdhury, N., Kabir, M., Rahman, M., and Islam, S. (2022). Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105405"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1007\/s10439-013-0741-6","article-title":"Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases","volume":"41","author":"Swarnkar","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vhaduri, S. (2020, January 15\u201317). Nocturnal Cough and Snore Detection Using Smartphones in Presence of Multiple Background-Noises. Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies, Guayaquil, Ecuador.","DOI":"10.1145\/3378393.3402273"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Teyhouee, A., and Osgood, N. (2019). Cough Detection Using Hidden Markov Models. arXiv.","DOI":"10.1007\/978-3-030-21741-9_27"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e18082","DOI":"10.2196\/18082","article-title":"Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study","volume":"22","author":"Barata","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TBME.2018.2849502","article-title":"Automatic Croup Diagnosis Using Cough Sound Recognition","volume":"66","author":"Sharan","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/JBHI.2018.2800741","article-title":"Robust Detection of Audio-Cough Events Using Local Hu Moments","volume":"23","author":"Lesso","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nemati, E., Rahman, M.M., Nathan, V., Vatanparvar, K., and Kuang, J. (2020, January 20\u201324). A Comprehensive Approach for Classification of the Cough Type. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175345"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kobat, M.A., Kivrak, T., Barua, P.D., Tuncer, T., Dogan, S., Tan, R.S., Ciaccio, E.J., and Acharya, U.R. (2021). Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds. Diagnostics, 11.","DOI":"10.3390\/diagnostics11111962"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bales, C., Nabeel, M., John, C.N., Masood, U., Qureshi, H.N., Farooq, H., Posokhova, I., and Imran, A. (2020, January 29\u201330). Can Machine Learning Be Used to Recognize and Diagnose Coughs?. Proceedings of the 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania.","DOI":"10.1109\/EHB50910.2020.9280115"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khomsay, S., Vanijjirattikhan, R., and Suwatthikul, J. (2019, January 16\u201318). Cough detection using PCA and Deep Learning. Proceedings of the 2019 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Republic of Korea.","DOI":"10.1109\/ICTC46691.2019.8939769"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sharan, R.V., Abeyratne, U.R., Swarnkar, V.R., and Porter, P. (2017, January 11\u201315). Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8037875"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pahar, M., Klopper, M., Warren, R., and Niesler, T. (2021). COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings. Comput. Biol. Med., 135.","DOI":"10.1016\/j.compbiomed.2021.104572"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tena, A., Clari\u00e0, F., and Solsona, F. (2022). Automated detection of COVID-19 cough. Biomed. Signal Process. Control., 71.","DOI":"10.1016\/j.bspc.2021.103175"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pahar, M., Klopper, M., Reeve, B., Theron, G., Warren, R., and Niesler, T. (2021). Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment. arXiv.","DOI":"10.1088\/1361-6579\/ac2fb8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17621","DOI":"10.1007\/s00521-021-06346-3","article-title":"Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound","volume":"33","author":"Melek","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xue, H., and Salim, F.D. (2021). Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification. arXiv.","DOI":"10.1145\/3447548.3467263"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hee, H.I., Balamurali, B., Karunakaran, A., Herremans, D., Teoh, O.H., Lee, K.P., Teng, S.S., Lui, S., and Chen, J.M. (2019). Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study. Appl. Sci., 9.","DOI":"10.3390\/app9142833"},{"key":"ref_37","unstructured":"Chaudhari, G., Jiang, X., Fakhry, A., Han, A., Xiao, J., Shen, S., and Khanzada, A. (2020). Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough. arXiv."},{"key":"ref_38","unstructured":"Xia, T., Spathis, D., Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Bondareva, E., Dang, T., and Floto, A. (2021, January 29). COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening. Proceedings of the Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sharma, N., Krishnan, P., Kumar, R., Ramoji, S., Chetupalli, S.R., Nirmala, R., Ghosh, P.K., and Ganapathy, S. (2020). Coswara\u2014A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis. arXiv, Available online: http:\/\/arxiv.org\/abs\/2005.10548.","DOI":"10.21437\/Interspeech.2020-2768"},{"key":"ref_40","unstructured":"Wang, W. (2021). The Corp Dataset. IEEE Dataport."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"154087","DOI":"10.1109\/ACCESS.2020.3018028","article-title":"Novel Coronavirus (2019) Cough Database: NoCoCoDa","volume":"8","author":"Goubran","year":"2020","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., and Schuller, B. (2010, January 25\u201329). Opensmile: The Munich Versatile and Fast Open-Source Audio Feature Extractor. Proceedings of the 18th ACM International Conference on Multimedia, MM \u201910, Firenze, Italy.","DOI":"10.1145\/1873951.1874246"},{"key":"ref_43","first-page":"445","article-title":"Smartphone based human breath analysis from Respiratory Sounds","volume":"2018","author":"Azam","year":"2018","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Basu, V., and Rana, S. (2020, January 27\u201329). Respiratory diseases recognition through respiratory sound with the help of deep neural network. Proceedings of the 2020 4th International Conference on Computational Intelligence and Networks (CINE), Kolkata, India.","DOI":"10.1109\/CINE48825.2020.234388"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gelman, A., Sokolovsky, V., Furman, E., Kalinina, N., and Furman, G. (2021). Artificial intelligence in the respiratory sounds analysis and computer diagnostics of bronchial asthma. medRxiv.","DOI":"10.1101\/2021.11.18.21266503"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10916-019-1388-0","article-title":"Respiratory sound based classification of chronic obstructive pulmonary disease: A risk stratification approach in machine learning paradigm","volume":"43","author":"Haider","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"155710","DOI":"10.1109\/ACCESS.2020.3016748","article-title":"Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning","volume":"8","author":"Meng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Id, M.A., and Khandoker, A.H. (2022). Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262448"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., and Mascolo, C. (2020, January 6\u201310). Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3412865"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nguyen, T., and Pernkopf, F. (2021). Lung Sound Classification Using Co-tuning and Stochastic Normalization. arXiv.","DOI":"10.1109\/TBME.2022.3156293"},{"key":"ref_51","unstructured":"G\u00f3mez, A.F.R., and Orjuela-Ca\u00f1\u00f3n, A.D. (2021, January 26\u201328). Respiratory Sounds Classification employing a Multi-label Approach. Proceedings of the 2021 IEEE Colombian Conference on Applications of Computational Intelligence, (ColCACI), Cali, Colombia."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Falah, A., and Jondri, J. (2019, January 24\u201326). Lung Sounds Classification Using Stacked Autoencoder and Support Vector Machine. Proceedings of the 2019 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICoICT.2019.8835278"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"32845","DOI":"10.1109\/ACCESS.2019.2903859","article-title":"Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"22018","DOI":"10.1109\/ACCESS.2022.3151789","article-title":"Respiratory Sound Classification: From Fluid-Solid Coupling Analysis to Feature-Band Attention","volume":"10","author":"Tong","year":"2022","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wu, L., and Li, L. (2020, January 20\u201324). Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175783"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ingco, W.E.M., Reyes, R.S., and Abu, P.A.R. (2019, January 6\u20139). Development of a Spectral Feature Extraction using Enhanced MFCC for Respiratory Sound Analysis. Proceedings of the 2019 International SoC Design Conference (ISOCC), Jeju, Republic of Korea.","DOI":"10.1109\/ISOCC47750.2019.9027640"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tariq, Z., Shah, S.K., and Lee, Y. (2019, January 18\u201321). Lung Disease Classification using Deep Convolutional Neural Network. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983071"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"139438","DOI":"10.1109\/ACCESS.2019.2943492","article-title":"Lung Sound Recognition Algorithm Based on VGGish-BiGRU","volume":"7","author":"Shi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lin, Y., Zhang, X., Wang, Z., Gao, Y., Chen, G., and Xiong, H. (2017, January 4\u20138). Classifying respiratory sounds using electronic stethoscope. Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), San Francisco, CA, USA.","DOI":"10.1109\/UIC-ATC.2017.8397496"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, Y.X., Yang, Y., and Chen, Y.H. (2017, January 12\u201315). Lung sound classification based on Hilbert-Huang transform features and multilayer perceptron network. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282137"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"17186","DOI":"10.1038\/s41598-021-96724-7","article-title":"Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Gairola, S., Tom, F., Kwatra, N., and Jain, M. (2021). RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting. arXiv.","DOI":"10.1109\/EMBC46164.2021.9630091"},{"key":"ref_63","unstructured":"de Mesquita Guimar\u00e3es e Ferreira Cardoso, H. (2021). Pulmonary Auscultation Using Mobile Devices\u2014Feasibility Study in Respiratory Diseases. [Doctoral Dissertation, Universidade do Porto]."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Rocha, B.M., Pessoa, D., Marques, A., Carvalho, P., and Paiva, R.P. (2021). Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?. Sensors, 21.","DOI":"10.3390\/s21010057"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Petmezas, G., Cheimariotis, G.A., Stefanopoulos, L., Rocha, B., Paiva, R.P., Katsaggelos, A.K., and Maglaveras, N. (2022). Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors, 22.","DOI":"10.3390\/s22031232"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Islam, M.A., Bandyopadhyaya, I., Bhattacharyya, P., and Saha, G. (2018, January 3\u20135). Classification of Normal, Asthma and COPD Subjects Using Multichannel Lung Sound Signals. Proceedings of the 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2018.8524439"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nguyen, T., and Pernkopf, F. (2020, January 20\u201324). Lung sound classification using snapshot ensemble of convolutional neural networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176076"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Naqvi, S.Z.H., Choudhary, M.A., Tariq, Z., and Waseem, A. (2020, January 12\u201313). Automated Detection and Classification of Multichannel Lungs Signals using EMD. Proceedings of the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey.","DOI":"10.1109\/ICECCE49384.2020.9179244"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Maglaveras, N., Chouvarda, I., and de Carvalho, P. (2018). Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, Thessaloniki, Greece, 18\u201321 November 2017, Springer.","DOI":"10.1007\/978-981-10-7419-6"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"webreport1743","DOI":"10.1186\/ccf-2000-webreport1743","article-title":"Acoustics of breathing","volume":"4","author":"Waterworth","year":"2000","journal-title":"Crit. Care"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"19149","DOI":"10.1038\/s41598-021-98742-x","article-title":"Identifying individuals with recent COVID-19 through voice classification using Deep Learning","volume":"11","author":"Suppakitjanusant","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"11143","DOI":"10.1007\/s13369-021-06041-4","article-title":"Artificial intelligence techniques for the non-invasive detection of COVID-19 through the analysis of Voice Signals","volume":"48","author":"Verde","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"65750","DOI":"10.1109\/ACCESS.2021.3075571","article-title":"Exploring the Use of Artificial Intelligence Techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis","volume":"9","author":"Verde","year":"2021","journal-title":"IEEE Access"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1121\/10.0003434","article-title":"Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires","volume":"149","author":"Shimon","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Nallanthighal, V.S., H\u00e4rm\u00e4, A., and Strik, H. (2022, January 23\u201327). Detection of COPD Exacerbation from Speech: Comparison of Acoustic Features and Deep Learning Based Speech Breathing Models. Proceedings of the ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747785"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"van Bemmel, L., Harmsen, W., Cucchiarini, C., and Strik, H. (2021, January 27\u201330). Automatic Selection of the Most Characterizing Features for Detecting COPD in Speech. Proceedings of the Speech and Computer: 23rd International Conference, SPECOM, St Petersburg, Russia.","DOI":"10.1007\/978-3-030-87802-3_66"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Muguli, A., Pinto, L., R., N., Sharma, N., Krishnan, P., Ghosh, P.K., Kumar, R., Bhat, S., Chetupalli, S.R., and Ganapathy, S. (2021). DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics. arXiv.","DOI":"10.21437\/Interspeech.2021-74"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/OJEMB.2020.3026468","article-title":"SARS-COV-2 detection from voice","volume":"1","author":"Pinkas","year":"2020","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3934\/publichealth.2021019","article-title":"Automatic covid-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: Cough, breath, and voice","volume":"8","author":"Lella","year":"2021","journal-title":"AIMS Public Health"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e6856","DOI":"10.1002\/cpe.6856","article-title":"An optimal asthma disease detection technique for voice signal using hybrid machine learning technique","volume":"34","author":"Devarajan","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Iqbal, M.A., Devarajan, K., and Ahmed, S.M. (2022). Real time detection and forecasting technique for asthma disease using speech signal and DENN classifier. Biomed. Signal Process. Control., 76.","DOI":"10.1016\/j.bspc.2022.103637"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Yadav, S., Keerthana, M., Gope, D., Maheswari, K.U., and Kumar Ghosh, P. (2020, January 4\u20138). Analysis of Acoustic Features for Speech Sound Based Classification of Asthmatic and Healthy Subjects. Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054062"},{"key":"ref_83","unstructured":"Rashid, M., Alman, K.A., Hasan, K., Hansen, J.H.L., and Hasan, T. (2020). Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features. arXiv."},{"key":"ref_84","first-page":"4595","article-title":"EAP-DL: Enhanced asthma prediction with voice recording using efficient feature extraction and classification technique","volume":"12","author":"Iqbal","year":"2021","journal-title":"Int. J. Aquat. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2273\/1\/012013","article-title":"Comparison of different classification techniques for the detection of speech affected due to respiratory disorders","volume":"2273","author":"Shrivastava","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Das, S. (2019, January 29\u201331). A Machine Learning Model for Detecting Respiratory Problems using Voice Recognition. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India.","DOI":"10.1109\/I2CT45611.2019.9033920"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"107999","DOI":"10.1016\/j.patcog.2021.107999","article-title":"Detection of COVID-19 from speech signal using bio-inspired based cepstral features","volume":"117","author":"Dash","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Liu, A.T., Yang, S.-w., Chi, P.-H., Hsu, P.-c., and Lee, H.-y. (2020, January 4\u20138). Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders. Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054458"},{"key":"ref_89","first-page":"341","article-title":"PRAAT, a system for doing phonetics by computer","volume":"5","author":"Boersma","year":"2001","journal-title":"Glot Int."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Schuller, B.W., Batliner, A., Amiriparian, S., Bergler, C., Gerczuk, M., Holz, N., Larrouy-Maestri, P., Bayerl, S.P., Riedhammer, K., and Mallol-Ragolta, A. (2022). The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes. arXiv.","DOI":"10.1145\/3503161.3551591"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). librosa: Audio and music signal analysis in python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41597-021-00937-4","article-title":"The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms","volume":"8","author":"Orlandic","year":"2021","journal-title":"Sci. Data"},{"key":"ref_93","unstructured":"Sait, U., Kv, G., Shivakumar, S., Kumar, T., Bhaumik, R., Prakash Prajapati, S., and Bhalla, K. (2021). Spectrogram Images of Breathing Sounds for COVID-19 and other Pulmonary Abnormalities. Mendeley Data."},{"key":"ref_94","unstructured":"Pizzo, D.T., and Esteban, S. (2021). IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples. arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TBCAS.2022.3204910","article-title":"SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database","volume":"16","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Hsu, F.S., Huang, S.R., Huang, C.W., Cheng, Y.R., Chen, C.C., Hsiao, J., Chen, C.W., and Lai, F. (2022). A Progressively Expanded Database for Automated Lung Sound Analysis: An Update. Appl. Sci., 12.","DOI":"10.3390\/app12157623"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 26\u201330). ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd ACM International Conference on Multimedia, MM \u201915, Brisbane, Australia.","DOI":"10.1145\/2733373.2806390"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5\u20139). Audio set: An ontology and human-labeled dataset for audio events. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhang, J., Yuan, J., Huang, H., Zhang, Y., Zhang, B., Lv, G., Lin, S., Wang, N., and Liu, X. (2022, January 13\u201315). Grand Challenge on Respiratory Sound Classification for SPRSound Dataset. Proceedings of the 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Taipei, Taiwan.","DOI":"10.1109\/BioCAS54905.2022.9948551"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kilintzis, V., Beredimas, N., Kaimakamis, E., Stefanopoulos, L., Chatzis, E., Jahaj, E., Bitzani, M., Kotanidou, A., Katsaggelos, A.K., and Maglaveras, N. (2022). CoCross: An ICT platform enabling monitoring recording and fusion of clinical information chest sounds and imaging of COVID-19 ICU patients. Proc. 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