TY - GEN
T1 - AN OVERVIEW OF THE FIRST ICASSP SPECIAL SESSION ON COMPUTER AUDITION FOR HEALTHCARE
AU - Qian, Kun
AU - Schultz, Tanja
AU - Schuller, Björn W.
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - Audio has been increasingly used as a novel digital phenotype that carries important information of the subject's health status. We can find tremendous efforts given to this young and promising field, i. e., computer audition for healthcare (CA4H), whereas the application scenarios have not been fully studied as compared to its counterpart in medical areas, computer vision. To this end, the first special session held at ICASSP 2020 was dedicated to the topic. In this overview paper, we at first summarise the invited high-quality contributions from leading scientists from a multi- disciplinary background. Then, we provide a detailed grouping of the contributions to several scenarios such as body sound analysis (e. g., heart sound), human speech analysis (e. g., stress detection), and artificial hearing technologies (e. g., cochlear implants). In addition to the collected works, we will compare them with other recent studies within the topic. Finally, we conclude the limitations and perspectives of the current stage. It is interesting and encouraging to find that the state-of-the-art machine learning and audio signal processing techniques have been successfully applied in the health domain, e. g., to fight with the global challenges of COVID-19 and ageing population.
AB - Audio has been increasingly used as a novel digital phenotype that carries important information of the subject's health status. We can find tremendous efforts given to this young and promising field, i. e., computer audition for healthcare (CA4H), whereas the application scenarios have not been fully studied as compared to its counterpart in medical areas, computer vision. To this end, the first special session held at ICASSP 2020 was dedicated to the topic. In this overview paper, we at first summarise the invited high-quality contributions from leading scientists from a multi- disciplinary background. Then, we provide a detailed grouping of the contributions to several scenarios such as body sound analysis (e. g., heart sound), human speech analysis (e. g., stress detection), and artificial hearing technologies (e. g., cochlear implants). In addition to the collected works, we will compare them with other recent studies within the topic. Finally, we conclude the limitations and perspectives of the current stage. It is interesting and encouraging to find that the state-of-the-art machine learning and audio signal processing techniques have been successfully applied in the health domain, e. g., to fight with the global challenges of COVID-19 and ageing population.
KW - Computer Audition
KW - Digital Phenotype
KW - Healthcare
KW - Intelligent Medicine
KW - Overview
UR - http://www.scopus.com/inward/record.url?scp=85131267505&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747333
DO - 10.1109/ICASSP43922.2022.9747333
M3 - Conference contribution
AN - SCOPUS:85131267505
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 9002
EP - 9006
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -
