Abstract
Crowdsourcing has become a common approach for annotating large amounts of data. It has the advantage of harnessing a large workforce to produce large amounts of data in a short time, but comes with the disadvantage of employing non-expert annotators with different backgrounds. This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth. This paper presents a study of the annotations and annotators' reliability for audio tagging. We adapt the use of Krippendorf's alpha and multi-annotator competence estimation (MACE) for a multi-labeled data scenario, and present how MACE can be used to estimate a candidate ground truth based on annotations from non-expert users with different levels of expertise and competence.
| Original language | English |
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| Title of host publication | 29th European Signal Processing Conference EUSIPCO 2021 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-9-0827-9706-0 |
| DOIs | |
| Publication status | Published - 2021 |
| Publication type | A4 Article in conference proceedings |
| Event | European Signal Processing Conference - Dublin, Ireland Duration: 23 Aug 2021 → 27 Aug 2021 https://eusipco2021.org |
Conference
| Conference | European Signal Processing Conference |
|---|---|
| Abbreviated title | EUSIPCO |
| Country/Territory | Ireland |
| City | Dublin |
| Period | 23/08/21 → 27/08/21 |
| Internet address |
Publication forum classification
- Publication forum level 1