{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:37:28Z","timestamp":1778755048878,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Algerian Ministry of Higher Education","award":["2019"],"award-info":[{"award-number":["2019"]}]},{"name":"University of Oulu including Oulu University Hospital"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>COVID-19 pandemic has fueled the interest in artificial intelligence tools for quick diagnosis to limit virus spreading. Over 60% of people who are infected complain of a dry cough. Cough and other respiratory sounds were used to build diagnosis models in much recent research. We propose in this work, an augmentation pipeline which is applied on the pre-filtered data and uses i) pitch-shifting technique to augment the raw signal and, ii) spectral data augmentation technique SpecAugment to augment the computed mel-spectrograms. A deep learning based architecture that hybridizes convolution neural networks and long-short term memory with an attention mechanism is proposed for building the classification model. The feasibility of the proposed is demonstrated through a set of testing scenarios using the large-scale COUGHVID cough dataset and through a comparison with three baselines models. We have shown that our classification model achieved 91.13% of testing accuracy, 90.93% of sensitivity and an area under the curve of receiver operating characteristic of 91.13%.<\/jats:p>","DOI":"10.1007\/s10844-022-00707-7","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T10:02:42Z","timestamp":1650708162000},"page":"367-389","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound"],"prefix":"10.1007","volume":"59","author":[{"given":"Skander","family":"Hamdi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4422-8723","authenticated-orcid":false,"given":"Mourad","family":"Oussalah","sequence":"additional","affiliation":[]},{"given":"Abdelouahab","family":"Moussaoui","sequence":"additional","affiliation":[]},{"given":"Mohamed","family":"Saidi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"707_CR1","unstructured":"Who coronavirus disease (covid-19) dashboard. (2021). https:\/\/covid19.who.int\/ Accessed 15 December."},{"key":"707_CR2","unstructured":"Who coronavirus disease health topics. (2021). https:\/\/www.who.int\/health-topics\/coronavirus Accessed 16 December."},{"key":"707_CR3","doi-asserted-by":"publisher","unstructured":"COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks. (2020). medRxiv https:\/\/doi.org\/10.1101\/2020.05.22.20110817https:\/\/www.medrxiv.org\/content\/early\/2020\/05\/24\/2020.05.22.20110817.","DOI":"10.1101\/2020.05.22.20110817"},{"issue":"2","key":"707_CR4","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai, T, Yang, Z, Hou, H, Zhan, C, Chen, C, Lv, W, Tao, Q, Sun, Z, & Xia, L (2020). Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology, 296(2), E32\u2013E40. https:\/\/doi.org\/10.1148\/radiol.2020200642.","journal-title":"Radiology"},{"key":"707_CR5","doi-asserted-by":"crossref","unstructured":"Amrulloh, Y, Abeyratne, U, Swarnkar, V, & Triasih, R (2015). Cough Sound Analysis for Pneumonia and Asthma Classification in Pediatric Population. In 2015 6th International Conference on Intelligent Systems, Modelling and Simulation (pp. 127\u2013131).","DOI":"10.1109\/ISMS.2015.41"},{"key":"707_CR6","doi-asserted-by":"publisher","first-page":"103795","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"121","author":"AA Ardakani","year":"2020","unstructured":"Ardakani, A A, Kanafi, A R, Acharya, U R, Khadem, N, & Mohammadi, A (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0010482520301645.","journal-title":"Computers in Biology and Medicine"},{"key":"707_CR7","doi-asserted-by":"crossref","unstructured":"Asif, S, Wenhui, Y, Jin, H, Tao, Y, & Jinhai, S. (2020). Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks.","DOI":"10.1101\/2020.05.01.20088211"},{"key":"707_CR8","doi-asserted-by":"crossref","unstructured":"Berrimi, M, Hamdi, S, Cherif, R Y, Moussaoui, A, Oussalah, M, & Chabane, M (2021). COVID-19 detection from Xray and CT scans using transfer learning. In 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ) (pp. 1\u20136).","DOI":"10.1109\/WiDSTaif52235.2021.9430229"},{"key":"707_CR9","doi-asserted-by":"publisher","unstructured":"Brown, C, Chauhan, J, Grammenos, A, Han, J, Hasthanasombat, A, Spathis, D, Xia, T, Cicuta, P, & Mascolo, C (2020). Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD \u201920. https:\/\/doi.org\/10.1145\/3394486.3412865 (pp. 3474\u20133484). New York, NY, USA: Association for Computing Machinery.","DOI":"10.1145\/3394486.3412865"},{"key":"707_CR10","doi-asserted-by":"crossref","unstructured":"Chatrzarrin, H, Arcelus, A, Goubran, R, & Knoefel, F (2011). Feature extraction for the differentiation of dry and wet cough sounds. 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Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification. arXiv:2105.07566.","DOI":"10.1145\/3447548.3467263"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00707-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-022-00707-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00707-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T17:26:29Z","timestamp":1662657989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-022-00707-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,23]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["707"],"URL":"https:\/\/doi.org\/10.1007\/s10844-022-00707-7","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,23]]},"assertion":[{"value":"9 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}