A time delay convolutional neural network for acoustic scene classification

Younglo Lee; Sangwook Park; Hanseok Ko

doi:10.1109/ICCE.2018.8326082

A time delay convolutional neural network for acoustic scene classification

Younglo Lee, Sangwook Park, Hanseok Ko

School of Electrical Engineering

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

1 Citation (Scopus)

Abstract

In recent years, demands for more natural interaction between human and machine through speech have been increasing. In order to accomplish this mission, it becomes more significant for machine to understand the human's contextual status. This paper proposes a novel neural network framework that can be applied to commercial smart devices with microphones to recognize acoustic contextual information. Our approach takes into consideration the fact that an acoustic signal has more local connectivity on the time axis than the frequency axis. Experimental results show that the proposed method outperforms two conventional approaches, which are Gaussian Mixture Models (GMMs) and Multi-Layer Perceptron (MLP), by 8.6% and 7.8% respectively in overall accuracy.

Original language	English
Title of host publication	2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Editors	Saraju P. Mohanty, Peter Corcoran, Hai Li, Anirban Sengupta, Jong-Hyouk Lee
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781538630259
DOIs	https://doi.org/10.1109/ICCE.2018.8326082
Publication status	Published - 2018 Mar 26
Event	2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States Duration: 2018 Jan 12 → 2018 Jan 14

Publication series

Name	2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Volume	2018-January

Other

Other	2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Country/Territory	United States
City	Las Vegas
Period	18/1/12 → 18/1/14

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Media Technology

Access to Document

10.1109/ICCE.2018.8326082

Cite this

Lee, Y., Park, S., & Ko, H. (2018). A time delay convolutional neural network for acoustic scene classification. In S. P. Mohanty, P. Corcoran, H. Li, A. Sengupta, & J-H. Lee (Eds.), 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 (pp. 1-3). (2018 IEEE International Conference on Consumer Electronics, ICCE 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2018.8326082

A time delay convolutional neural network for acoustic scene classification. / Lee, Younglo; Park, Sangwook; Ko, Hanseok.
2018 IEEE International Conference on Consumer Electronics, ICCE 2018. ed. / Saraju P. Mohanty; Peter Corcoran; Hai Li; Anirban Sengupta; Jong-Hyouk Lee. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3 (2018 IEEE International Conference on Consumer Electronics, ICCE 2018; Vol. 2018-January).

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

Lee, Y, Park, S & Ko, H 2018, A time delay convolutional neural network for acoustic scene classification. in SP Mohanty, P Corcoran, H Li, A Sengupta & J-H Lee (eds), 2018 IEEE International Conference on Consumer Electronics, ICCE 2018. 2018 IEEE International Conference on Consumer Electronics, ICCE 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-3, 2018 IEEE International Conference on Consumer Electronics, ICCE 2018, Las Vegas, United States, 18/1/12. https://doi.org/10.1109/ICCE.2018.8326082

Lee Y, Park S, Ko H. A time delay convolutional neural network for acoustic scene classification. In Mohanty SP, Corcoran P, Li H, Sengupta A, Lee J-H, editors, 2018 IEEE International Conference on Consumer Electronics, ICCE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-3. (2018 IEEE International Conference on Consumer Electronics, ICCE 2018). doi: 10.1109/ICCE.2018.8326082

Lee, Younglo ; Park, Sangwook ; Ko, Hanseok. / A time delay convolutional neural network for acoustic scene classification. 2018 IEEE International Conference on Consumer Electronics, ICCE 2018. editor / Saraju P. Mohanty ; Peter Corcoran ; Hai Li ; Anirban Sengupta ; Jong-Hyouk Lee. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-3 (2018 IEEE International Conference on Consumer Electronics, ICCE 2018).

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title = "A time delay convolutional neural network for acoustic scene classification",

abstract = "In recent years, demands for more natural interaction between human and machine through speech have been increasing. In order to accomplish this mission, it becomes more significant for machine to understand the human's contextual status. This paper proposes a novel neural network framework that can be applied to commercial smart devices with microphones to recognize acoustic contextual information. Our approach takes into consideration the fact that an acoustic signal has more local connectivity on the time axis than the frequency axis. Experimental results show that the proposed method outperforms two conventional approaches, which are Gaussian Mixture Models (GMMs) and Multi-Layer Perceptron (MLP), by 8.6% and 7.8% respectively in overall accuracy.",

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AB - In recent years, demands for more natural interaction between human and machine through speech have been increasing. In order to accomplish this mission, it becomes more significant for machine to understand the human's contextual status. This paper proposes a novel neural network framework that can be applied to commercial smart devices with microphones to recognize acoustic contextual information. Our approach takes into consideration the fact that an acoustic signal has more local connectivity on the time axis than the frequency axis. Experimental results show that the proposed method outperforms two conventional approaches, which are Gaussian Mixture Models (GMMs) and Multi-Layer Perceptron (MLP), by 8.6% and 7.8% respectively in overall accuracy.

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A time delay convolutional neural network for acoustic scene classification

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