# Crowdsourcing strong labels for sound event detection

arXiv: https://arxiv.org/abs/2107.12089 · HTML: https://mlsanity.com/paper/2107.12089/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 3 of 3)

- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
- [From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning](https://mlsanity.com/paper/2403.08525/) (2024)
- [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)

## Abstract (arXiv)

Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task. We present a method for estimating strong labels using crowdsourced weak labels, through a process that divides the annotation task into simple unit tasks. Based on estimations of annotators' competence, aggregation and processing of the weak labels results in a set of objective strong labels. The experiment uses synthetic audio in order to verify the quality of the resulting annotations through comparison with ground truth. The proposed method produces labels with high precision, though not all event instances are recalled. Detection metrics comparing the produced annotations with the ground truth show 80% F-score in 1 s segments, and up to 89.5% intersection-based F1-score calculated according to the polyphonic sound detection score metrics.