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Acoustic scene classification: An overview of dcase 2017 challenge entries

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

Details

Original languageEnglish
Title of host publication16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
PublisherIEEE
Pages411-415
Number of pages5
ISBN (Electronic)9781538681510
DOIs
Publication statusPublished - 2 Nov 2018
Publication typeA4 Article in a conference publication
EventInternational Workshop on Acoustic Signal Enhancement - Tokyo, Japan
Duration: 17 Sep 201820 Sep 2018

Conference

ConferenceInternational Workshop on Acoustic Signal Enhancement
CountryJapan
CityTokyo
Period17/09/1820/09/18

Abstract

We present an overview of the challenge entries for the Acoustic Scene Classification task of DCASE 2017 Challenge. Being the most popular task of the challenge, acoustic scene classification entries provide a wide variety of approaches for comparison, with a wide performance gap from top to bottom. Analysis of the submissions confirms once more the popularity of deep-learning approaches and mel frequency representations. Statistical analysis indicates that the top ranked system performed significantly better than the others, and that combinations of top systems are capable of reaching close to perfect performance on the given data.

Keywords

  • Acoustic scene classification, Audio classb ification, DCASE challenge

Publication forum classification

Field of science, Statistics Finland