Assessment of human and machine performance in acoustic scene classification: dcase 2016 case study

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    Abstract

    Human and machine performance in acoustic scene classification is examined through a parallel experiment using TUT Acoustic Scenes 2016 dataset. The machine learning perspective is presented based on the systems submitted for the 2016 challenge on Detection and Classification of Acoustic Scenes and Events. The human performance, assessed through a listening experiment, was found to be significantly lower than machine performance. Test subjects exhibited different behavior throughout the experiment, leading to significant differences in performance between groups of subjects. An expert listener trained for the task obtained similar accuracy to the average of submitted systems, comparable also to previous studies of human abilities in recognizing everyday acoustic scenes.
    Original languageEnglish
    Title of host publication2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
    PublisherIEEE Computer Society
    Pages 319–323
    ISBN (Print)978-1-5386-1631-4
    DOIs
    Publication statusPublished - 2017
    Publication typeA4 Article in a conference publication
    EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics -
    Duration: 1 Jan 1900 → …

    Conference

    ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
    Period1/01/00 → …

    Keywords

    • acoustic scene classification
    • machine learning
    • human performance
    • listening experiment

    Publication forum classification

    • Publication forum level 1

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