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In the break of COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is…

We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not…

声音 · 计算机科学 2022-05-12 Madhurananda Pahar , Marisa Klopper , Robin Warren , Thomas Niesler

Just like your phone can detect what song is playing in crowded spaces, we show that Artificial Intelligence transfer learning algorithms trained on cough phone recordings results in diagnostic tests for COVID-19. To gain adoption by the…

The disease called the new coronavirus (COVID19) is a new viral respiratory disease that first appeared on January 13, 2020 in Wuhan, China. Some of the symptoms of this disease are fever, cough, shortness of breath and difficulty in…

音频与语音处理 · 电气工程与系统科学 2022-01-19 Yunus Emre Erdoğan , Ali Narin

The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by…

Recent advancements in deep learning techniques have sparked performance boosts in various real-world applications including disease diagnosis based on multi-modal medical data. Cough sound data-based respiratory disease (e.g., COVID-19 and…

计算机视觉与模式识别 · 计算机科学 2024-09-04 Qian Wang , Zhaoyang Bu , Jiaxuan Mao , Wenyu Zhu , Jingya Zhao , Wei Du , Guochao Shi , Min Zhou , Si Chen , Jieming Qu

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few…

声音 · 计算机科学 2021-12-16 Kranthi Kumar Lella , Alphonse PJA

This report describes our submission to BHI 2023 Data Competition: Sensor challenge. Our Audio Alchemists team designed an acoustic-based COVID-19 diagnosis system, Cough to COVID-19 (C2C), and won the 1st place in the challenge. C2C…

音频与语音处理 · 电气工程与系统科学 2023-11-02 Woo-Jin Chung , Miseul Kim , Hong-Goo Kang

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are…

音频与语音处理 · 电气工程与系统科学 2020-05-15 Jing Han , Kun Qian , Meishu Song , Zijiang Yang , Zhao Ren , Shuo Liu , Juan Liu , Huaiyuan Zheng , Wei Ji , Tomoya Koike , Xiao Li , Zixing Zhang , Yoshiharu Yamamoto , Björn W. Schuller

Fast and affordable solutions for COVID-19 testing are necessary to contain the spread of the global pandemic and help relieve the burden on medical facilities. Currently, limited testing locations and expensive equipment pose difficulties…

声音 · 计算机科学 2021-03-18 Ahmed Fakhry , Xinyi Jiang , Jaclyn Xiao , Gunvant Chaudhari , Asriel Han , Amil Khanzada

Cough is a common symptom of respiratory and lung diseases. Cough detection is important to prevent, assess and control epidemic, such as COVID-19. This paper proposes a model to detect cough events from cough audio signals. The models are…

声音 · 计算机科学 2021-08-10 Xinru Chen , Menghan Hu , Guangtao Zhai

Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice. Different researchers use different kinds of information from the voice signal to achieve this. Various types of phonated sounds and the sound…

声音 · 计算机科学 2022-10-27 Ankit Shah , Hira Dhamyal , Yang Gao , Daniel Arancibia , Mario Arancibia , Bhiksha Raj , Rita Singh

Rapid and affordable methods of testing for COVID-19 infections are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed. Current approaches of detecting COVID-19 require in-person testing with…

The Coronavirus (COVID-19) pandemic has been the research focus world-wide in the year 2020. Several efforts, from collection of COVID-19 patients' data to screening them for the virus's detection are taken with rigour. A major portion of…

声音 · 计算机科学 2020-12-01 Gauri Deshpande , Björn W. Schuller

SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in December 2019. The deadly virus has spread across the world, taking part in the global pandemic disease since March 2020. In addition, a recent variant of…

COVID-19 has affected more than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19…

声音 · 计算机科学 2026-05-13 Yuyang Yan , Wafaa Aljbawi , Sami O. Simons , Visara Urovi

In this study, we proposed a machine learning-based system to distinguish patients with COVID-19 from non-COVID-19 patients by analyzing only a single cough sound. Two different data sets were used, one accessible for the public and the…

声音 · 计算机科学 2021-02-10 Masoud Maleki

Millions of people have died worldwide from COVID-19. In addition to its high death toll, COVID-19 has led to unbearable suffering for individuals and a huge global burden to the healthcare sector. Therefore, researchers have been trying to…

声音 · 计算机科学 2023-11-14 Sudip Vhaduri , Seungyeon Paik , Jessica E Huber

This paper addresses the issue of cough detection using only audio recordings, with the ultimate goal of quantifying and qualifying the degree of pathology for patients suffering from respiratory diseases, notably mucoviscidosis. A large…

声音 · 计算机科学 2020-01-06 Thomas Drugman , Jerome Urbain , Thierry Dutoit

We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant…

机器学习 · 计算机科学 2022-09-13 Madhurananda Pahar , Marisa Klopper , Byron Reeve , Rob Warren , Grant Theron , Andreas Diacon , Thomas Niesler