Considering the trend of the vehicle market where the vehicle becomes quieter, in-vehicle rattling noise is significant criterion for the quality of the vehicle. Though the latest deep learning algorithms have been introduced for classifying in-vehicle rattling noise, there are limitations due to impulsive and transient nature of rattling noise and reflective and refractive characteristics of in-vehicle environment. In this paper, we propose a novel beamforming method that extracts intra-interchannel spatial features by parameterizing the optimal beamforming weights including Direction-of-Arrival (DOA) function to overcome the addressed problem. The proposed method outperformed the existing deep learning algorithms with 0.9270 accuracy and verified by 10-fold cross validation and chi-squared test. In addition, it is shown that the time cost for classification of rattling noise is appropriate for real-time classification as a side-effect of using convolution-pooling operations.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2019 Dec|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: 2019 Dec 9 → 2019 Dec 12
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||19/12/9 → 19/12/12|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by grant funded by 2019 IT promotion fund (Development of AI based Precision Medicine Emergency System) of the Korean government (Ministry of Science and ICT).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management