A Summary of the ComParE COVID-19 Challenges

Coppock, Harry; Akman, Alican; Bergler, Christian; Gerczuk, Maurice; Brown, Chloë; Chauhan, Jagmohan; Grammenos, Andreas; Hasthanasombat, Apinan; Spathis, Dimitris; Tong, Xia; Cicuta, Pietro; Han, Jianda; Amiriparian, Shahin; Baird, Alice; Stappen, Lukas; Ottl, Sandra; Tzirakis, Panagiotis; Batliner, Anton; Mascolo, Cecilia; Schuller, Björn W.

doi:10.48550/arxiv.2202.08981

“…Three processing blocks empirically improve that baseline method largely, including improvements from the original baseline methods [20]. A thorough summary without our result is also can be found in [41], in which the authors also discuss the result of speech-based COVID-19 detection in addition to the cough-based method.…”

Section: Effect Of Data Augmentationmentioning

confidence: 68%

Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation

Atmaja¹,

Zanjabila²,

Suyanto³

2022

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“…We manually checked the quality of each audio file and found that 30 COVID-positive recordings in ComParE were with lower sampling rates (< 12kHz). Including these files could lead to overly-optimistic COVID-19 prediction results, thus they were removed from our analyses as suggested by [6].…”

Section: Cough Sound Datasetsmentioning

confidence: 99%

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

Zhu¹,

Shaik²,

Falk³

2022

Preprint

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<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>

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“…Two cough sound datasets are employed in this study, namely the ComParE [6] and DiCOVA2 [8] datasets. In both datasets, participants were asked to make forced coughs for several times in a quiet environment.…”

Section: Cough Sound Datasetsmentioning

confidence: 99%

“…In fact, several speech and cough-based detection challenges have been held since the outburst of COVID-19, including the INTERSPEECH 2021 Computational Paralinguistics Challenge (ComParE) [6] and the Diagnosis of COVID-19 using Acoustics (DiCOVA) [7], [8] Challenge series, where participating teams developed COVID-19 detection systems based on cough, speech, and breathing sound recordings collected from subjects around the world. The majority of existing studies relied on the mel-spectrogram, mel-frequency cepstral coefficients (MFCC), and/or other classical features computed by the openSMILE toolbox [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…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]. As can be seen, there is still ample room for improvement.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu¹,

Shaik²,

Falk³

2022

Preprint

0

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

A Summary of the ComParE COVID-19 Challenges

Cited by 6 publications

References 0 publications

Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation

Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation

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

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

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