This is a dataset containing audio captions and corresponding audio tags for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool. Each file is annotated by multiple annotators that provided tags and a one-sentence description of the audio content. The data also includes annotator competence estimated using MACE (Multi-Annotator Competence Estimation). The annotation procedure, processing and analysis of the data are presented in the following papers: Irene Martin-Morato, Annamaria Mesaros. What is the ground truth? Reliability of multi-annotator data for audio tagging, 29th European Signal Processing Conference, EUSIPCO 2021 Irene Martin-Morato, Annamaria Mesaros. Diversity and bias in audio captioning datasets, submitted to DCASE 2021 Workshop (to be updated with arxiv link) Data is provided as two files: MACS.yaml - containing the complete annotations in the following format: - filename: file1.wav
annotations:
- annotator_id: ann_1
sentence: caption text
tags:
- tag1
- tag2
- annotator_id: ann_2
sentence: caption text
tags:
- tag1 MACS_competence.csv - containing the estimated annotator competence; for each annotator_id in the yaml file, competence is a number between 0 (considered as annotating at random) and 1 id [tab] competence The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.

This is a dataset containing audio captions and corresponding audio tags for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool. Each file is annotated by multiple annotators that provided tags and a one-sentence description of the audio content.

This is a dataset containing audio tags for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool, with multiple annotators providing labels for each file. The dataset contains annotations for 3930 files, annotated with the following tags: announcement jingle announcement speech adults talking birds singing children voices dog barking footsteps music siren traffic noise The annotation procedure and processing is presented in the paper: Irene Martin-Morato, Annamaria Mesaros. What is the ground truth? Reliability of multi-annotator data for audio tagging, 29th European Signal Processing Conference, EUSIPCO 2021 The dataset contains the following: raw annotations provided by 133 annotators, multiple opinions per audio file MATS_labels_full_annotations.yaml content formatted as: - filename: file1.wav
annotations:
- annotator_id: ann_1
tags:
- tag1
- tag2
- annotator_id: ann_3
tags:
- tag1
- filename: file3.wav
... processed annotations using different methods, as presented in the accompanying paper MATS_labels_majority_vote.csv
MATS_labels_union.csv
MATS_labels_mace100.csv
MATS_labels_mace100_competence60 content formatted as: filename [tab] tag1,tag2,tag3
The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.