Acoustic scene classification: An overview of dcase 2017 challenge entries

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

    31 Citations (Scopus)
    2 Downloads (Pure)

    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.

    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
    Country/TerritoryJapan
    CityTokyo
    Period17/09/1820/09/18

    Keywords

    • Acoustic scene classification
    • Audio classb ification
    • DCASE challenge

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Signal Processing
    • Acoustics and Ultrasonics

    Fingerprint

    Dive into the research topics of 'Acoustic scene classification: An overview of dcase 2017 challenge entries'. Together they form a unique fingerprint.

    Cite this