TY - GEN
T1 - Data Augmentation Methods for Electric Automobile Noise Design from Multi-Channel Steering Accelerometer Signals
AU - Jo, Yongwon
AU - Jeong, Keewon
AU - Ahn, Sihu
AU - Koh, Eunji
AU - Ko, Eunsung
AU - Kim, Seoung Bum
N1 - Funding Information:
Acknowledgments. This research was supported by the Brain Korea 21 FOUR, Ministry of Science and ICT (MSIT) in Korea under the ITRC support program (IITP-2020-0-01749) supervised by the Information & communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea grant funded by the MSIT (NRF-2019R1A4A1024732).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Noise, vibration, and harshness (NVH) of electric automobiles is important because the loud NVH can reduce the satisfaction of automobile drivers and passengers. Therefore, the effective machine learning models to alleviate NVH is required. Although a huge amount of data is needed to construct the reliable models, the number of training data is very scarce in practice. In this paper, we propose a deep learning model combined with data augmentation methods (dropout and SpecAugment) that predicts interior noise levels from steering accelerometer signals when only a small number of training data is available. The effectiveness of the proposed framework was demonstrated using steering automobile accelerometer signals and noise levels from real automobiles.
AB - Noise, vibration, and harshness (NVH) of electric automobiles is important because the loud NVH can reduce the satisfaction of automobile drivers and passengers. Therefore, the effective machine learning models to alleviate NVH is required. Although a huge amount of data is needed to construct the reliable models, the number of training data is very scarce in practice. In this paper, we propose a deep learning model combined with data augmentation methods (dropout and SpecAugment) that predicts interior noise levels from steering accelerometer signals when only a small number of training data is available. The effectiveness of the proposed framework was demonstrated using steering automobile accelerometer signals and noise levels from real automobiles.
KW - Data augmentation
KW - Deep learning
KW - Electric automobile
KW - Noise
KW - Steering accelerometer
UR - http://www.scopus.com/inward/record.url?scp=85137986681&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16072-1_49
DO - 10.1007/978-3-031-16072-1_49
M3 - Conference contribution
AN - SCOPUS:85137986681
SN - 9783031160714
T3 - Lecture Notes in Networks and Systems
SP - 679
EP - 684
BT - Intelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2022
Y2 - 1 September 2022 through 2 September 2022
ER -