Cough-based COVID-19 Detection with Multi-band Long-Short Term Memory and Convolutional Neural Networks

Shen, Jiakun; Zhang, Xueshuai; Wang, Qianqian; Huang, Zhihua; Zhang, Pengyuan; Yan, Yonghong

doi:10.1145/3500931.3500968

Cited by 3 publications

(2 citation statements)

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“…MFCC features were used in 62% of the studies, although the number of extracted coefficients varied extensively among the papers. Five of the studies [ 32 , 37 , 40 , 50 , 54 ] did not report the number of coefficients that were extracted, and two papers [ 29 , 55 ] mentioned the use of over 60 coefficients. In most cases, 12 or 13 coefficients were used.…”

Section: Resultsmentioning

confidence: 99%

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The Use of Audio Signals for Detecting COVID-19: A Systematic Review

Aleixandre

Elgendi

Menon

2022

Sensors

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A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

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Section: Resultsmentioning

confidence: 99%

“…Some of the studies integrated these three types into ensembles. Alkhodari and Khandoker [ 57 ] and Shen et al [ 55 ] combined a CNN with an RNN, while Sanjeev et al [ 52 ] and Fakhry et al [ 58 ] used an FNN together with a CNN. Son and Lee [ 59 ] employed a Dense Neural Network (DNN) in combination with a CNN.…”

Section: Resultsmentioning

confidence: 99%