A survey of deep learning for polyphonic sound event detection

An Dang, Toan H. Vu, Jia Ching Wang

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

12 Scopus citations

Abstract

Deep learning has achieved state of the art in various machine learning problems, such as computer vision, speech recognition, and natural language processing. Sound event detection (SED), which is about recognizing audio events in real-life environments, has attracted a lot of attention recently. Many works have been successful when applying deep learning techniques for the SED problem as can be seen in Detection and Classification of Acoustic Scenes and Events (DCASE) challenge 2016-2017. In this paper, we present a review of the SED problem and discuss different deep learning approaches for the problem.

Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
EditorsLei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-78
Number of pages4
ISBN (Electronic)9781538632758
DOIs
StatePublished - 10 Apr 2018
Event5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
Duration: 8 Dec 201710 Dec 2017

Publication series

NameProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Volume2018-January

Conference

Conference5th International Conference on Orange Technologies, ICOT 2017
Country/TerritorySingapore
CitySingapore
Period8/12/1710/12/17

Keywords

  • Convolutional neural networks
  • Deep learning
  • Neural networks
  • Recurrent neural networks
  • Sound event detection

Fingerprint

Dive into the research topics of 'A survey of deep learning for polyphonic sound event detection'. Together they form a unique fingerprint.

Cite this