TY - GEN
T1 - An Investigation on Data Augmentation and Multiple Instance Learning for Diagnosis of COVID-19 from Speech and Cough Sound
AU - Koike, Tomoya
AU - Wang, Zhihua
AU - Qian, Kun
AU - Hu, Bin
AU - Schuller, Björn W.
AU - Yamamoto, Yoshiharu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Computer audition based approaches for diagnosing COVID-19 can provide a low-cost, convenient, and real-time solution for combating the ongoing global pandemic. In this contribution, we present an investigation on data augmentation and multiple instance learning methods for diagnosis of COVID-19 from speech and cough sound data. We firstly introduce a novel deep convolutional neural network pre-trained on large scale audio data set, i. e., AudioSet. Moreover, we use a multiple instance learning paradigm to address the training difficulties caused by the varied length of the audio instances. Experimental results demonstrate the efficiency of the proposed methods, which can reach a best performance at 75.9 % of the unweighted average recall, surpassing the official baseline single best by 3.0 % and baseline fusion best by 2.0 %.
AB - Computer audition based approaches for diagnosing COVID-19 can provide a low-cost, convenient, and real-time solution for combating the ongoing global pandemic. In this contribution, we present an investigation on data augmentation and multiple instance learning methods for diagnosis of COVID-19 from speech and cough sound data. We firstly introduce a novel deep convolutional neural network pre-trained on large scale audio data set, i. e., AudioSet. Moreover, we use a multiple instance learning paradigm to address the training difficulties caused by the varied length of the audio instances. Experimental results demonstrate the efficiency of the proposed methods, which can reach a best performance at 75.9 % of the unweighted average recall, surpassing the official baseline single best by 3.0 % and baseline fusion best by 2.0 %.
UR - http://www.scopus.com/inward/record.url?scp=85174920416&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226678
DO - 10.1109/ICCE-Taiwan58799.2023.10226678
M3 - Conference contribution
AN - SCOPUS:85174920416
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 783
EP - 784
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -