University of Oulu

Hamdi, S., Oussalah, M., Moussaoui, A. et al. Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound. J Intell Inf Syst 59, 367–389 (2022).

Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound

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Author: Hamdi, Skander1; Oussalah, Mourad2; Moussaoui, Abdelouahab1;
Organizations: 1Department of Computer Science, University of Ferhat Abbes Setif, 19000, Setif, Algeria
2Department of Computer Science and Engineering, University of Oulu, 90570, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2022-05-12


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%.

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Series: Journal of intelligent information systems
ISSN: 0925-9902
ISSN-E: 1573-7675
ISSN-L: 0925-9902
Volume: 59
Pages: 367 - 389
DOI: 10.1007/s10844-022-00707-7
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: Open Access funding provided by University of Oulu including Oulu University Hospital. This work is partly supported by the Algerian Ministry of Higher Education, which is gratefully acknowledged.
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