Crowdsourcing a Dataset of Audio Captions

Samuel Lipping, Konstantinos Drossos, Tuomas Virtanen

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


Audio captioning is a novel field of multi-modal translation and it is the task of creating a textual description of the content of an audio signal (e.g. "people talking in a big room"). The creation of a dataset for this task requires a considerable amount of work, rendering the crowdsourcing a very attractive option. In this paper we present a three steps based framework for crowdsourcing an audio captioning dataset, based on concepts and practises followed for the creation of widely used image captioning and machine translations datasets. During the first step initial captions are gathered. A grammatically corrected and/or rephrased version of each initial caption is obtained in second step. Finally, the initial and edited captions are rated, keeping the top ones for the produced dataset. We objectively evaluate the impact of our framework during the process of creating an audio captioning dataset, in terms of diversity and amount of typographical errors in the obtained captions. The obtained results show that the resulting dataset has less typographical errors than the initial captions, and on average each sound in the produced dataset has captions with a Jaccard similarity of 0.24, roughly equivalent to two ten-word captions having in common four words with the same root, indicating that the captions are dissimilar while they still contain some of the same information.
Original languageEnglish
Title of host publicationProceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)
ISBN (Electronic)978-0-578-59596-2
Publication statusPublished - 26 Oct 2019
Publication typeA4 Article in conference proceedings
EventWorkshop on Detection and Classification of Acoustic Scenes and Events - New York, United States
Duration: 25 Oct 201926 Oct 2019


WorkshopWorkshop on Detection and Classification of Acoustic Scenes and Events
Abbreviated titleDCASE
Country/TerritoryUnited States
CityNew York


  • audio captioning
  • captioning
  • amt
  • crowdsourcing
  • Amazon Mechanical Turk

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