# Crowdsourcing a Dataset of Audio Captions

arXiv: https://arxiv.org/abs/1907.09238 · HTML: https://mlsanity.com/paper/1907.09238/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning](https://mlsanity.com/paper/2110.07410/) (2021)
- [Continual Learning for Automated Audio Captioning Using The Learning Without Forgetting Approach](https://mlsanity.com/paper/2107.08028/) (2021)
- [Caption Feature Space Regularization for Audio Captioning](https://mlsanity.com/paper/2204.08409/) (2022)
- [Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity](https://mlsanity.com/paper/2210.06354/) (2022)
- [A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning](https://mlsanity.com/paper/2405.18153/) (2024)
- [Make Some Noise: Towards LLM audio reasoning and generation using sound tokens](https://mlsanity.com/paper/2503.22275/) (2025)

## Abstract (arXiv)

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.