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.
|Title of host publication||Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021|
|Editors||Herwig 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|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2021 Jan|
|Event||2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021 - Jeju Island, Korea, Republic of|
Duration: 2021 Jan 17 → 2021 Jan 20
|Name||Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021|
|Conference||2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021|
|Country||Korea, Republic of|
|Period||21/1/17 → 21/1/20|
Bibliographical noteFunding 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.
2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and the Korea Electric Power Corporation (Grant number: R18XA05).
© 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