In spite of various deep learning models devised, it is still a challenging task to classify in-vehicle noise because of the reverberation and the variance in the low-frequency band generated from the narrow interior space. Considering the impulsive characteristics of the vehicle noise and the multi-channel sampling environment at the same time, it is essential to automatically learn the disentangled noise representation as well as parameterize the conventional beamforming operation. We propose a method to overcome the above two major hurdles by parameterizing a beamforming operation based on convolutional neural network. Moreover, we improve the structure of the beamforming network by explicitly learning of the distance between vehicle noises within the triplet network framework. Experiments with the dataset consisting of a total 241,958,848 time-series collected by a global motor company show that the proposed model improves the classification accuracy by 5% compared to the latest deep acoustic models. The detailed analysis shows that the proposed method can potentially compensate for the disjoint issues between the learning and validation vehicle types.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings|
|Editors||Cesar Analide, Paulo Novais, David Camacho, Hujun Yin|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||9|
|Publication status||Published - 2020|
|Event||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal|
Duration: 2020 Nov 4 → 2020 Nov 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020|
|Period||20/11/4 → 20/11/6|
Bibliographical noteFunding Information:
Acknowledgments. This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Hyundai Motors, Inc.
This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Hyundai Motors, Inc.
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)