Parallel capsule neural networks for sound event detection

Kai Wen Liang; Yu Hao Tseng; Pao Chi Chang

doi:10.1109/APSIPAASC47483.2019.9023176

Parallel capsule neural networks for sound event detection

Kai Wen Liang, Yu Hao Tseng, Pao Chi Chang

通訊工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

3 引文斯高帕斯（Scopus）

摘要

In this work, we propose a sound event detection system based on a parallel capsule neural network. The system takes advantage of the capability of capsule neural networks in the detection of overlapping objects. It further develops a parallel architecture and uses the kernel design of different shapes and sizes to effectively utilize the feature information to increase the detection accuracy. The experimental results show that the performance of the proposed system is as low as 52.34% measured by the error rate, which is even lower than the rank 1 system in DCASE2017 challenge.

原文	???core.languages.en_GB???
主出版物標題	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	1933-1936
頁數	4
ISBN（電子）	9781728132488
DOIs	https://doi.org/10.1109/APSIPAASC47483.2019.9023176
出版狀態	已出版 - 11月 2019
事件	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China 持續時間: 18 11月 2019 → 21 11月 2019

出版系列

名字	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
國家/地區	China
城市	Lanzhou
期間	18/11/19 → 21/11/19

存取文件

10.1109/APSIPAASC47483.2019.9023176

智慧型照護互動系統－基於深度智能之口語處理技術(2/4)
Wang, J.-C. (PI)
1/01/19 → 31/12/19
研究計畫: Research
自駕車之深度學習智能感知與情境理解系統技術－自駕車之深度學習智能感知與情境理解系統技術-子計畫四
Chang, P.-C. (PI)
1/12/18 → 31/07/19
研究計畫: Research

引用此

Liang, K. W., Tseng, Y. H., & Chang, P. C. (2019). Parallel capsule neural networks for sound event detection. 於 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 (頁 1933-1936). 文章 9023176 (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC47483.2019.9023176

Liang, Kai Wen ; Tseng, Yu Hao ; Chang, Pao Chi. / Parallel capsule neural networks for sound event detection. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 頁 1933-1936 (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019).

@inproceedings{673643e695fe41d38c1af3d5c7b5aacf,

title = "Parallel capsule neural networks for sound event detection",

abstract = "In this work, we propose a sound event detection system based on a parallel capsule neural network. The system takes advantage of the capability of capsule neural networks in the detection of overlapping objects. It further develops a parallel architecture and uses the kernel design of different shapes and sizes to effectively utilize the feature information to increase the detection accuracy. The experimental results show that the performance of the proposed system is as low as 52.34% measured by the error rate, which is even lower than the rank 1 system in DCASE2017 challenge.",

keywords = "Capsule Neural Network, Computational Auditory Scene Analysis, Deep Learning, Sound Event Detection",

author = "Liang, {Kai Wen} and Tseng, {Yu Hao} and Chang, {Pao Chi}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/APSIPAASC47483.2019.9023176",

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series = "2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Liang, KW, Tseng, YH & Chang, PC 2019, Parallel capsule neural networks for sound event detection. 於 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019., 9023176, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019, Institute of Electrical and Electronics Engineers Inc., 頁 1933-1936, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019, Lanzhou, China, 18/11/19. https://doi.org/10.1109/APSIPAASC47483.2019.9023176

Parallel capsule neural networks for sound event detection. / Liang, Kai Wen; Tseng, Yu Hao; Chang, Pao Chi.
2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1933-1936 9023176 (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

TY - GEN

T1 - Parallel capsule neural networks for sound event detection

AU - Liang, Kai Wen

AU - Tseng, Yu Hao

AU - Chang, Pao Chi

PY - 2019/11

Y1 - 2019/11

N2 - In this work, we propose a sound event detection system based on a parallel capsule neural network. The system takes advantage of the capability of capsule neural networks in the detection of overlapping objects. It further develops a parallel architecture and uses the kernel design of different shapes and sizes to effectively utilize the feature information to increase the detection accuracy. The experimental results show that the performance of the proposed system is as low as 52.34% measured by the error rate, which is even lower than the rank 1 system in DCASE2017 challenge.

AB - In this work, we propose a sound event detection system based on a parallel capsule neural network. The system takes advantage of the capability of capsule neural networks in the detection of overlapping objects. It further develops a parallel architecture and uses the kernel design of different shapes and sizes to effectively utilize the feature information to increase the detection accuracy. The experimental results show that the performance of the proposed system is as low as 52.34% measured by the error rate, which is even lower than the rank 1 system in DCASE2017 challenge.

KW - Capsule Neural Network

KW - Computational Auditory Scene Analysis

KW - Deep Learning

KW - Sound Event Detection

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U2 - 10.1109/APSIPAASC47483.2019.9023176

DO - 10.1109/APSIPAASC47483.2019.9023176

M3 - 會議論文篇章

AN - SCOPUS:85082386879

T3 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

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EP - 1936

BT - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Y2 - 18 November 2019 through 21 November 2019

ER -

Liang KW, Tseng YH, Chang PC. Parallel capsule neural networks for sound event detection. 於 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1933-1936. 9023176. (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019). doi: 10.1109/APSIPAASC47483.2019.9023176

Parallel capsule neural networks for sound event detection

摘要

出版系列

???event.eventtypes.event.conference???

存取文件

指紋

專案

智慧型照護互動系統－基於深度智能之口語處理技術(2/4)

自駕車之深度學習智能感知與情境理解系統技術－自駕車之深度學習智 能感知與情境理解系統技術-子計畫四

引用此

自駕車之深度學習智能感知與情境理解系統技術－自駕車之深度學習智能感知與情境理解系統技術-子計畫四