Adversarial signal augmentation for CNN-LSTM to classify impact noise in automobiles

Seok Jun Bu, Hyung Jun Moon, Sung Bae Cho

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

Abstract

The classification of impact noise on vehicle steering gear mainly addresses the issue of modeling the transient and impulsive signals. In particular, variations between the steering systems arising from the differences in manufacturing processes according to the vehicle types extremely limit the conventional deep acoustic models. Focusing on the fact that the major hurdles addressed can be mitigated by generating and modeling the virtual impact noise, we propose an adversarial signal augmentation method for the vehicle noise modeling. The impact noise is represented based on the Fourier transform and the variance between vehicle types is alleviated using a generative adversarial network with an auxiliary classifier in order to improve the generalization performance of the model. Experiments with the dataset of 134, 400, 000 time-series collected from a global motor corporation show that the proposed method has more than 3% of accuracy improvement against the conventional approaches.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021
EditorsHerwig Unger, Jinho Kim, U Kang, Chakchai So-In, Junping Du, Walid Saad, Young-guk Ha, Christian Wagner, Julien Bourgeois, Chanboon Sathitwiriyawong, Hyuk-Yoon Kwon, Carson Leung
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-64
Number of pages5
ISBN (Electronic)9781728189246
DOIs
Publication statusPublished - 2021 Jan
Event2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 172021 Jan 20

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021

Conference

Conference2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021
CountryKorea, Republic of
CityJeju Island
Period21/1/1721/1/20

Bibliographical note

Funding Information:
This work was partly supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No.

Funding Information:
2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and the Korea Electric Power Corporation (Grant number: R18XA05).

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing
  • Information Systems and Management

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