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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.08981">arXiv:2202.08981</a> <span> [<a href="https://arxiv.org/pdf/2202.08981">pdf</a>, <a href="https://arxiv.org/format/2202.08981">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> A Summary of the ComParE COVID-19 Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Coppock%2C+H">Harry Coppock</a>, <a href="/search/cs?searchtype=author&query=Akman%2C+A">Alican Akman</a>, <a href="/search/cs?searchtype=author&query=Bergler%2C+C">Christian Bergler</a>, <a href="/search/cs?searchtype=author&query=Gerczuk%2C+M">Maurice Gerczuk</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Amiriparian%2C+S">Shahin Amiriparian</a>, <a href="/search/cs?searchtype=author&query=Baird%2C+A">Alice Baird</a>, <a href="/search/cs?searchtype=author&query=Stappen%2C+L">Lukas Stappen</a>, <a href="/search/cs?searchtype=author&query=Ottl%2C+S">Sandra Ottl</a>, <a href="/search/cs?searchtype=author&query=Tzirakis%2C+P">Panagiotis Tzirakis</a>, <a href="/search/cs?searchtype=author&query=Batliner%2C+A">Anton Batliner</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a>, <a href="/search/cs?searchtype=author&query=Schuller%2C+B+W">Bj枚rn W. Schuller</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.08981v1-abstract-short" style="display: inline;"> 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 class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.08981v1-abstract-full').style.display = 'inline'; document.getElementById('2202.08981v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.08981v1-abstract-full" style="display: none;"> 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). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.08981v1-abstract-full').style.display = 'none'; document.getElementById('2202.08981v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 13 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.01232">arXiv:2201.01232</a> <span> [<a href="https://arxiv.org/pdf/2201.01232">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.2196/37004">10.2196/37004 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dang%2C+T">Ting Dang</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Bondareva%2C+E">Erika Bondareva</a>, <a href="/search/cs?searchtype=author&query=Siegele-Brown%2C+C">Chlo毛 Siegele-Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Floto%2C+A">Andres Floto</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.01232v2-abstract-short" style="display: inline;"> Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, thro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01232v2-abstract-full').style.display = 'inline'; document.getElementById('2201.01232v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.01232v2-abstract-full" style="display: none;"> Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics could lead to more timely treatment. The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed. We developed a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests. The strong performance for COVID-19 detection, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, displaying high consistency with test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort who reported recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01232v2-abstract-full').style.display = 'none'; document.getElementById('2201.01232v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Updated title. Revised format according to journal requirements</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.15523">arXiv:2106.15523</a> <span> [<a href="https://arxiv.org/pdf/2106.15523">pdf</a>, <a href="https://arxiv.org/format/2106.15523">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Sounds of COVID-19: exploring realistic performance of audio-based digital testing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Bondareva%2C+E">Erika Bondareva</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Dang%2C+T">Ting Dang</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Floto%2C+A">Andres Floto</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.15523v1-abstract-short" style="display: inline;"> Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio based approaches, which collect respiratory audio data (cough, breathing and voice) can be used for testing, however there is a lack of exploration of how biases and methodological decisions impact these tools' performanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.15523v1-abstract-full').style.display = 'inline'; document.getElementById('2106.15523v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.15523v1-abstract-full" style="display: none;"> Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio based approaches, which collect respiratory audio data (cough, breathing and voice) can be used for testing, however there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside recent COVID-19 test result and symptoms intended as a ground truth. Within the collected dataset, we selected 5,240 samples from 2,478 participants and split them into different participant-independent sets for model development and validation. Among these, we controlled for potential confounding factors (such as demographics and language). The unbiased model takes features extracted from breathing, coughs, and voice signals as predictors and yields an AUC-ROC of 0.71 (95\% CI: 0.65$-$0.77). We further explore different unbalanced distributions to show how biases and participant splits affect performance. Finally, we discuss how the realistic model presented could be integrated in clinical practice to realize continuous, ubiquitous, sustainable and affordable testing at population scale. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.15523v1-abstract-full').style.display = 'none'; document.getElementById('2106.15523v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.13468">arXiv:2102.13468</a> <span> [<a href="https://arxiv.org/pdf/2102.13468">pdf</a>, <a href="https://arxiv.org/format/2102.13468">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schuller%2C+B+W">Bj枚rn W. Schuller</a>, <a href="/search/cs?searchtype=author&query=Batliner%2C+A">Anton Batliner</a>, <a href="/search/cs?searchtype=author&query=Bergler%2C+C">Christian Bergler</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Lefter%2C+I">Iulia Lefter</a>, <a href="/search/cs?searchtype=author&query=Kaya%2C+H">Heysem Kaya</a>, <a href="/search/cs?searchtype=author&query=Amiriparian%2C+S">Shahin Amiriparian</a>, <a href="/search/cs?searchtype=author&query=Baird%2C+A">Alice Baird</a>, <a href="/search/cs?searchtype=author&query=Stappen%2C+L">Lukas Stappen</a>, <a href="/search/cs?searchtype=author&query=Ottl%2C+S">Sandra Ottl</a>, <a href="/search/cs?searchtype=author&query=Gerczuk%2C+M">Maurice Gerczuk</a>, <a href="/search/cs?searchtype=author&query=Tzirakis%2C+P">Panagiotis Tzirakis</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Rothkrantz%2C+L+J+M">Leon J. M. Rothkrantz</a>, <a href="/search/cs?searchtype=author&query=Zwerts%2C+J">Joeri Zwerts</a>, <a href="/search/cs?searchtype=author&query=Treep%2C+J">Jelle Treep</a>, <a href="/search/cs?searchtype=author&query=Kaandorp%2C+C">Casper Kaandorp</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.13468v1-abstract-short" style="display: inline;"> The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of es… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13468v1-abstract-full').style.display = 'inline'; document.getElementById('2102.13468v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.13468v1-abstract-full" style="display: none;"> The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13468v1-abstract-full').style.display = 'none'; document.getElementById('2102.13468v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; I.5.0; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.05225">arXiv:2102.05225</a> <span> [<a href="https://arxiv.org/pdf/2102.05225">pdf</a>, <a href="https://arxiv.org/format/2102.05225">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICASSP39728.2021.9414576">10.1109/ICASSP39728.2021.9414576 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.05225v1-abstract-short" style="display: inline;"> The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVI… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.05225v1-abstract-full').style.display = 'inline'; document.getElementById('2102.05225v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.05225v1-abstract-full" style="display: none;"> The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of $0.79$ has been attained, with a sensitivity of $0.68$ and a specificity of $0.82$. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.05225v1-abstract-full').style.display = 'none'; document.getElementById('2102.05225v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures, 2 tables, Accepted for publication at ICASSP 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.05919">arXiv:2006.05919</a> <span> [<a href="https://arxiv.org/pdf/2006.05919">pdf</a>, <a href="https://arxiv.org/format/2006.05919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3394486.3412865">10.1145/3394486.3412865 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.05919v3-abstract-short" style="display: inline;"> Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05919v3-abstract-full').style.display = 'inline'; document.getElementById('2006.05919v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.05919v3-abstract-full" style="display: none;"> Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05919v3-abstract-full').style.display = 'none'; document.getElementById('2006.05919v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 6 figures, 2 tables, Accepted for publication at KDD'20 (Health Day)</span> </p> </li> </ol> <div class="is-hidden-tablet">  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