  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.
