Data Augmentation Methods for Electric Automobile Noise Design from Multi-Channel Steering Accelerometer Signals

Yongwon Jo, Keewon Jeong, Sihu Ahn, Eunji Koh, Eunsung Ko, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages6
ISBN (Print)9783031160714
Publication statusPublished - 2023
EventIntelligent Systems Conference, IntelliSys 2022 - Virtual, Online
Duration: 2022 Sep 12022 Sep 2

Publication series

NameLecture Notes in Networks and Systems
Volume542 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceIntelligent Systems Conference, IntelliSys 2022
CityVirtual, Online


  • Data augmentation
  • Deep learning
  • Electric automobile
  • Noise
  • Steering accelerometer

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


Dive into the research topics of 'Data Augmentation Methods for Electric Automobile Noise Design from Multi-Channel Steering Accelerometer Signals'. Together they form a unique fingerprint.

Cite this