Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 From Audio Challenges

Akman, Alican; Coppock, Harry; Gaskell, Alexander; Tzirakis, Panagiotis; Jones, Lyn; Schuller, Björn W.

doi:10.3389/fdgth.2022.789980

Cited by 11 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process is complicated and time-consuming. In order to better assist clinicians in diagnosis, many works apply deep learning technology in the field of medical intelligence (Yuan et al 2019;Jia et al 2020;Akman et al 2021;Phan et al 2022;Olesen et al 2021;Piriyajitakonkij et al 2020;Feng et al 2021;Torres et al 2016). The intelligent system discovers disease patterns by learning historical data and various clinical indicators, supplemented by experts knowledge.…”

Section: Health Carementioning

confidence: 99%

See 1 more Smart Citation

Deep learning on multi-view sequential data: a survey

et al. 2022

View full text Add to dashboard Cite

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

show abstract

Section: Health Carementioning

confidence: 99%

“…In recent years, the coronavirus (COVID-19) has spread worldwide, causing a large number of human casualties and economic losses. Some works use coughing and breathing audio to determine whether COVID is positive or negative (Akman et al 2021;Nessiem et al 2021;Coppock et al 2021).…”

Section: Health Carementioning

confidence: 99%

Deep learning on multi-view sequential data: a survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These worries have been supported by findings that when sources of bias are controlled, the performance of the classifiers decreases ( 16 , 17 ). Along with this, cross dataset experiments have reported a marked drop in performance when models trained on one dataset are then evaluated on another dataset, suggesting dataset specific bias ( 18 ).…”

Section: Introductionmentioning

confidence: 91%

A summary of the ComParE COVID-19 challenges

Coppock

Akman²,

Bergler³

et al. 2023

Front. Digit. Health

View full text Add to dashboard Cite

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals’ respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

show abstract

“…Features, in turn, were computed across the entire cough recording. For classifiers deep, convolutional, and recurrent neural networks have shown high accuracy on both the Com-ParE and DiCOVA datasets [11]- [13]. Notwithstanding, it has been shown that such models can suffer from overfitting and provide limited generalizability across datasets [11], [14].…”

Section: Introductionmentioning

confidence: 99%

“…For classifiers deep, convolutional, and recurrent neural networks have shown high accuracy on both the Com-ParE and DiCOVA datasets [11]- [13]. Notwithstanding, it has been shown that such models can suffer from overfitting and provide limited generalizability across datasets [11], [14]. In fact, classical machine learning algorithms, such as a support vector machine (SVM), have shown to outperform deep neural network ones [6].…”

Section: Introductionmentioning

confidence: 99%

On the importance of different cough phases for COVID-19 detection

Zhu¹,

Shaik²,

Falk³

2022

Preprint

View full text Add to dashboard Cite

<p>Cough is an important symptom of numerous respiratory diseases, including COVID-19. While different cough phases (i.e., inhalation, compression, and expulsion) have been shown to be related to different pathological origins, existing cough-based COVID-19 detection systems rely on the entire cough recording, thus such phase-related characteristics are overlooked. In this study, our aim is two-fold. First, we have annotated over 1,250 cough recordings from two publicly-available cough sound databases, thus providing the research community with fine-grained cough phase labels. Next, we extract a number of temporal and acoustic features from each cough phase and test their usefulness and complementarity for COVID-19 detection. Experiments show the importance of cough phase segmentation, not only for improved COVID-19 detection, but also for the development of models that are interpretable and can better generalize across datasets.</p>

show abstract

Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 From Audio Challenges

Cited by 11 publications

References 26 publications

Deep learning on multi-view sequential data: a survey

Deep learning on multi-view sequential data: a survey

A summary of the ComParE COVID-19 challenges

On the importance of different cough phases for COVID-19 detection

Contact Info

Product

Resources

About