TY - GEN
T1 - Ambient Sound Analysis for Non-Invasive Indoor Activity Detection in Edge Computing Environments
AU - Lee, Cheolhwan
AU - Kang, Ho Min
AU - Jeon, Yeongjun
AU - Kang, Soon Ju
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Research on detecting the behavior of residents using sounds generated in living spaces has been conducted by sending the sound data to a server or cloud and utilizing a relatively large artificial intelligence model. However, this method generates excessive data traffic and carries a privacy risk by transmitting sounds unnecessary for behavior detection. In this paper, we explored data processing methods suitable for a non-invasive indoor noisy sound analysis system operating in an edge environment. To achieve this goal, we implemented Mel-spectrogram and Mel-Frequency Cepstral Coefficients (MFCC) based models for classifying environmental sounds, comparing their performance based on different preprocessing parameters and optimizations. Furthermore, we evaluated the computational resource usage and performance of the models in both the Raspberry Pi and microcontroller environments.
AB - Research on detecting the behavior of residents using sounds generated in living spaces has been conducted by sending the sound data to a server or cloud and utilizing a relatively large artificial intelligence model. However, this method generates excessive data traffic and carries a privacy risk by transmitting sounds unnecessary for behavior detection. In this paper, we explored data processing methods suitable for a non-invasive indoor noisy sound analysis system operating in an edge environment. To achieve this goal, we implemented Mel-spectrogram and Mel-Frequency Cepstral Coefficients (MFCC) based models for classifying environmental sounds, comparing their performance based on different preprocessing parameters and optimizations. Furthermore, we evaluated the computational resource usage and performance of the models in both the Raspberry Pi and microcontroller environments.
KW - Ambient sound analysis
KW - Edge computing
KW - Indoor activity detection
KW - Non-invasive sound analysis
UR - http://www.scopus.com/inward/record.url?scp=85171971522&partnerID=8YFLogxK
U2 - 10.1109/ISCC58397.2023.10217851
DO - 10.1109/ISCC58397.2023.10217851
M3 - Conference contribution
AN - SCOPUS:85171971522
T3 - Proceedings - IEEE Symposium on Computers and Communications
BT - ISCC 2023 - 28th IEEE Symposium on Computers and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE Symposium on Computers and Communications, ISCC 2023
Y2 - 9 July 2023 through 12 July 2023
ER -