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

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

Details

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 → …

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.

Keywords

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

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