Automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network

Seok Jun Bu, Sung Bae Cho

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

Abstract

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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings
EditorsCesar Analide, Paulo Novais, David Camacho, Hujun Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages507-515
Number of pages9
ISBN (Print)9783030623647
DOIs
Publication statusPublished - 2020
Event21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal
Duration: 2020 Nov 42020 Nov 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12490 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020
CountryPortugal
CityGuimaraes
Period20/11/420/11/6

Bibliographical note

Funding 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.

Funding Information:
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.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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