TY - GEN
T1 - AI Hears Your Health
T2 - 1st International Conference on ICT for Health, Accessibility and Wellbeing, IHAW 2021
AU - Amiriparian, Shahin
AU - Schuller, Björn
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Acoustic sounds produced by the human body reflect changes in our mental, physiological, and pathological states. A deep analysis of such audio that are of complex nature can give insight about imminent or existing health issues. For automatic processing and understanding of such data, sophisticated machine learning approaches are needed that can extract or learn robust features. In this paper, we introduce a set of machine learning toolkits both for supervised feature extraction and unsupervised representation learning from audio health data. We analyse the application of deep neural networks (DNNs), including end-to-end learning, recurrent autoencoders, and transfer learning for speech and body-acoustics health monitoring and provide state-of-the-art results for each area. As show-case examples, we pick three well-benchmarked examples for body-acoustics and speech, each, from the popular annual Interspeech Computational Paralinguistics Challenge (ComParE). In particular, the speech-based health tasks are COVID-19 speech analysis, recognition of upper respiratory tract infections, and continuous sleepiness recognition. The body-acoustics health tasks are COVID-19 cough analysis, speech breath monitoring, heartbeat abnormality recognition, and snore sound classification. The results for all tasks demonstrate the suitability of deep computer audition approaches for health monitoring and automatic audio-based early diagnosis of health issues.
AB - Acoustic sounds produced by the human body reflect changes in our mental, physiological, and pathological states. A deep analysis of such audio that are of complex nature can give insight about imminent or existing health issues. For automatic processing and understanding of such data, sophisticated machine learning approaches are needed that can extract or learn robust features. In this paper, we introduce a set of machine learning toolkits both for supervised feature extraction and unsupervised representation learning from audio health data. We analyse the application of deep neural networks (DNNs), including end-to-end learning, recurrent autoencoders, and transfer learning for speech and body-acoustics health monitoring and provide state-of-the-art results for each area. As show-case examples, we pick three well-benchmarked examples for body-acoustics and speech, each, from the popular annual Interspeech Computational Paralinguistics Challenge (ComParE). In particular, the speech-based health tasks are COVID-19 speech analysis, recognition of upper respiratory tract infections, and continuous sleepiness recognition. The body-acoustics health tasks are COVID-19 cough analysis, speech breath monitoring, heartbeat abnormality recognition, and snore sound classification. The results for all tasks demonstrate the suitability of deep computer audition approaches for health monitoring and automatic audio-based early diagnosis of health issues.
KW - Computer audition
KW - Digital health
KW - Health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85126379066&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-94209-0_20
DO - 10.1007/978-3-030-94209-0_20
M3 - Conference contribution
AN - SCOPUS:85126379066
SN - 9783030942083
T3 - Communications in Computer and Information Science
SP - 227
EP - 233
BT - ICT for Health, Accessibility and Wellbeing - 1st International Conference, IHAW 2021, Revised Selected Papers
A2 - Pissaloux, Edwige
A2 - Papadopoulos, George Angelos
A2 - Achilleos, Achilleas
A2 - Velázquez, Ramiro
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 November 2021 through 9 November 2021
ER -