# A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes

arXiv: https://arxiv.org/abs/2202.01487 · HTML: https://mlsanity.com/paper/2202.01487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [The Benefit Of Temporally-Strong Labels In Audio Event Classification](https://mlsanity.com/paper/2105.07031/) (2021)
- [A Framework for the Robust Evaluation of Sound Event Detection](https://mlsanity.com/paper/1910.08440/) (2019)
- [Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments](https://mlsanity.com/paper/1807.10501/) (2018)
- [Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes](https://mlsanity.com/paper/2011.00801/) (2020)
- [Training Sound Event Detection On A Heterogeneous Dataset](https://mlsanity.com/paper/2007.03931/) (2020)
- [Improving Sound Event Detection In Domestic Environments Using Sound Separation](https://mlsanity.com/paper/2007.03932/) (2020)
- [Heavily Augmented Sound Event Detection utilizing Weak Predictions](https://mlsanity.com/paper/2107.03649/) (2021)
- [The impact of non-target events in synthetic soundscapes for sound event detection](https://mlsanity.com/paper/2109.14061/) (2021)
- [Improving Sound Event Detection Metrics: Insights from DCASE 2020](https://mlsanity.com/paper/2010.13648/) (2020)
- [Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4](https://mlsanity.com/paper/2107.02569/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Event Extraction from Speech with Contextual Clues](https://mlsanity.com/paper/2401.15385/) (2024)

## Abstract (arXiv)

This paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task. The submissions are evaluated according to the two polyphonic sound detection score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to make an analysis on whether submissions are designed to perform fine-grained temporal segmentation, coarse-grained temporal segmentation, or have been designed to be polyvalent on the scenarios proposed. We study the solutions proposed by participants to analyze their robustness to varying level target to non-target signal-to-noise ratio and to temporal localization of target sound events. A last experiment is proposed in order to study the impact of non-target events on systems outputs. Results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and, with the help of specific data augmentation methods, they are more robust to time localization of the original event. Results of the last experiment display that systems tend to spuriously predict short events when non-target events are present. This is particularly true for systems that are tailored to have a fine segmentation.