Apinan Hasthanasombat
University of Cambridge
10 Papers
174 Citations
Apinan Hasthanasombat is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 4, co-authored 7 publications.
Chat about Author
Papers
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Jing Han,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Cecilia Mascolo +8 more
TL;DR: The results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds, and opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid CO VID-19 diagnosis.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Jing Han,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Cecilia Mascolo +8 more
- 23 Aug 2020
TL;DR: In this paper, a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19 was used to understand how discernible COVID19 sounds are from those in asthma or healthy controls.
170
Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data
Jing Han,Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Cecilia Mascolo +8 more
- 06 Jun 2021
TL;DR: In this paper, a voice-based framework was proposed to automatically detect individuals who have tested positive for COVID-19 from audio sounds, which could facilitate testing and prevent more costly clinical tests.
110
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 cough, COVID-19 speech, escalation & primates
Björn Schuller,Anton Batliner,Christian Bergler,Cecilia Mascolo,Jing Han,Iulia Lefter,Heysem Kaya,Shahin Amiriparian,Alice Baird,Lukas Stappen,Sandra Ottl,Maurice Gerczuk,Panagiotis Tzirakis,Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Léon J. M. Rothkrantz,Joeri A. Zwerts,Jelle Treep,Casper S. Kaandorp +23 more
- 24 Feb 2021
TL;DR: The INTERSPEECH 2021 Computational Paralinguistics Challenge as discussed by the authors addressed four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID19 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 Subchallenge, four species vs background need to be classified.
78
•Posted Content
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han,Tong Xia,Dimitris Spathis,Erika Bondareva,Chloë Brown,Jagmohan Chauhan,Ting Dang,Andreas Grammenos,Apinan Hasthanasombat,Andres Floto,Pietro Cicuta,Cecilia Mascolo +11 more
TL;DR: In this article, the authors explore the realistic performance of audio-based digital testing of COVID-19 cases and explore different unbalanced distributions to show how biases and participant splits affect performance, and discuss how the realistic model presented could be integrated in clinical practice to realize continuous, ubiquitous, sustainable and affordable testing at population scale.
65