Parallel capsule neural networks for sound event detection

Kai Wen Liang, Yu Hao Tseng, Pao Chi Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1933-1936
Number of pages4
ISBN (Electronic)9781728132488
DOIs
StatePublished - Nov 2019
Event2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
Duration: 18 Nov 201921 Nov 2019

Publication series

Name2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Country/TerritoryChina
CityLanzhou
Period18/11/1921/11/19

Keywords

  • Capsule Neural Network
  • Computational Auditory Scene Analysis
  • Deep Learning
  • Sound Event Detection

Fingerprint

Dive into the research topics of 'Parallel capsule neural networks for sound event detection'. Together they form a unique fingerprint.

Cite this