Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks

Seok Jun Bu; Sung Bae Cho

doi:10.1109/BigData47090.2019.9005960

Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks

Seok Jun Bu, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

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.

Original language	English
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.
Pages	3545-3552
Number of pages	8
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9005960
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

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	19/12/9 → 19/12/12

Bibliographical note

Funding 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).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management

Access to Document

10.1109/BigData47090.2019.9005960

Cite this

Bu, S. J., & Cho, S. B. (2019). Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 3545-3552). [9005960] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9005960

Bu, Seok Jun ; Cho, Sung Bae. / Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3545-3552 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{20d9452f3b86460c8ca501e87caf5211,

title = "Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks",

abstract = "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.",

author = "Bu, {Seok Jun} and Cho, {Sung Bae}",

note = "Funding 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). Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9005960",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

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editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

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}

Bu, SJ & Cho, SB 2019, Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9005960, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 3545-3552, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 19/12/9. https://doi.org/10.1109/BigData47090.2019.9005960

Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. / Bu, Seok Jun; Cho, Sung Bae.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3545-3552 9005960 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks

AU - Bu, Seok Jun

AU - Cho, Sung Bae

N1 - Funding 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). Publisher Copyright: © 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 3545

EP - 3552

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Bu SJ, Cho SB. Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3545-3552. 9005960. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9005960

Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks

Abstract

Publication series

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Bibliographical note

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