TY - GEN
T1 - Real-Time Sound Event Classification for Human Activity of Daily Living using Deep Neural Network
AU - Yuh, Ah Hyun
AU - Kang, Soon Ju
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Over the past years, increasing number of IoT sensors played important role in developing ambient assisted living (AAL) technologies such as elderly home care system by predicting activity of daily livings (ADLs). One way to develop smarter home care services with unobtrusive sensors in ubiquitous forms is using sound. This paper suggests a methodology to detect different sound events generated by residents based on real-life audio data. We propose a guide all the way from installing wireless microphone networks to recording, annotating, and preprocessing audios. Then we extract audio features and design deep learning classifier to classifying sound events. Finally, we deploy classifier on real-life scenarios to implement sound event detection in real-time. We evaluated 2D convolutional classifier with 16 sound events, achieving 95.55 % training accuracy, 94.64 % validation accuracy, 96.40 % recall score, and 94.93 % F1-score.
AB - Over the past years, increasing number of IoT sensors played important role in developing ambient assisted living (AAL) technologies such as elderly home care system by predicting activity of daily livings (ADLs). One way to develop smarter home care services with unobtrusive sensors in ubiquitous forms is using sound. This paper suggests a methodology to detect different sound events generated by residents based on real-life audio data. We propose a guide all the way from installing wireless microphone networks to recording, annotating, and preprocessing audios. Then we extract audio features and design deep learning classifier to classifying sound events. Finally, we deploy classifier on real-life scenarios to implement sound event detection in real-time. We evaluated 2D convolutional classifier with 16 sound events, achieving 95.55 % training accuracy, 94.64 % validation accuracy, 96.40 % recall score, and 94.93 % F1-score.
KW - Activity of Daily Living
KW - Audio Signal Pro-cessing
KW - Deep Learning
KW - Real Time System
KW - Sound Event Classification
UR - http://www.scopus.com/inward/record.url?scp=85127432348&partnerID=8YFLogxK
U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00027
DO - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00027
M3 - Conference contribution
AN - SCOPUS:85127432348
T3 - Proceedings - IEEE Congress on Cybermatics: 2021 IEEE International Conferences on Internet of Things, iThings 2021, IEEE Green Computing and Communications, GreenCom 2021, IEEE Cyber, Physical and Social Computing, CPSCom 2021 and IEEE Smart Data, SmartData 2021
SP - 83
EP - 88
BT - Proceedings - IEEE Congress on Cybermatics
A2 - Zheng, James
A2 - Liu, Xiao
A2 - Luan, Tom Hao
A2 - Jayaraman, Prem Prakash
A2 - Dai, Haipeng
A2 - Mitra, Karan
A2 - Qin, Kai
A2 - Ranjan, Rajiv
A2 - Wen, Sheng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Congress on Cybermatics: 14th IEEE International Conferences on Internet of Things, iThings 2021, 17th IEEE International Conference on Green Computing and Communications, GreenCom 2021, 2021 IEEE International Conference on Cyber Physical and Social Computing, CPSCom 2021 and 7th IEEE International Conference on Smart Data, SmartData 2021
Y2 - 6 December 2021 through 8 December 2021
ER -
