Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection

Rao, Sunil; Narayanaswamy, Vivek; Esposito, Michael; Thiagarajan, Jayaraman J.; Spanias, Andreas

doi:10.1109/iisa52424.2021.9555564

Cited by 21 publications

(7 citation statements)

References 22 publications

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“…However, these studies effectively removed the silence parts by hard thresholding. Rao et al [ 46 ] chose a simpler way; they considered any part of the recording below an amplitude threshold to be silent and limited the non-silence parts to a fixed-length duration. Gupta et al [ 47 ] applied a low-pass fourth-order Butterworth filter with a cut-off frequency of 6 kHz (cf.…”

Section: Resultsmentioning

confidence: 99%

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%

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

Aleixandre

Elgendi

Menon

2022

Sensors

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“…In terms of technical development, a few studies follow the traditional machine learning approaches with extracted audio features [20,21,22,10] In a similar approach, the study by [21] [23,24,25,11]. Rao et al [23] present a VGG13 network [26] on spectrogram with combined cross-entropy and focal loss. The approach achieves an AUC of 0.78 on the COUGHVID dataset.…”

Section: Screening Covid-19mentioning

confidence: 99%

FAIR4Cov: Fused Audio Instance and Representation for COVID-19 Detection

Truong¹,

Lenga²,

Serrurier³

et al. 2022

Preprint

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Audio-based classification techniques on body sounds have long been studied to support diagnostic decisions, particularly in pulmonary diseases. In response to the urgency of the COVID-19 pandemic, a growing number of models are developed to identify COVID-19 patients based on acoustic input. Most models focus on cough because the dry cough is the best-known symptom of COVID-19.However, other body sounds, such as breath and speech, have also been revealed to correlate with COVID-19 as well. In this work, rather than relying on a specific body sound, we propose Fused Audio Instance and Representation for COVID-19 Detection (FAIR4Cov). It relies on constructing a joint feature vector obtained from a plurality of body sounds in waveform and spectrogram representation. The core component of FAIR4Cov is a self-attention fusion unit that is trained to establish the relation of multiple body sounds and audio representations and integrate it into a compact feature vector. We set up our experiments on different combinations of body sounds using only waveform, spectrogram, and a joint representation of waveform and spectrogram. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. This AUC is 0.0227 higher than the one of the models trained on spectrograms only and 0.0847 higher than the one of the models trained on waveforms only. The results demonstrate that the

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“…The human respiratory system may be affected by pathological conditions which typically alter the patterns of the emitted sound events [31]. Recently, due to the COVID-19 pandemic, such kind of diseases became of primary importance for the public health, being a novel cause of death for large part of the population [25]. Interestingly, medical acoustics, i.e.…”

Section: Introductionmentioning

confidence: 99%

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

Cozzatti¹,

Simonetta²,

Ntalampiras³

2022

Preprint

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This paper proposes a weakly-supervised machine learningbased approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57%, which is in line with the existing strongly-supervised approaches.

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Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection

Cited by 21 publications

References 22 publications

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

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

FAIR4Cov: Fused Audio Instance and Representation for COVID-19 Detection

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

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