# The impact of non-target events in synthetic soundscapes for sound event detection

arXiv: https://arxiv.org/abs/2109.14061 · HTML: https://mlsanity.com/paper/2109.14061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [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)
- [Adaptive pooling operators for weakly labeled sound event detection](https://mlsanity.com/paper/1804.10070/) (2018)
- [What's All the FUSS About Free Universal Sound Separation Data?](https://mlsanity.com/paper/2011.00803/) (2020)
- [A Closer Look at Weak Label Learning for Audio Events](https://mlsanity.com/paper/1804.09288/) (2018)
- [Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments](https://mlsanity.com/paper/1807.10501/) (2018)
- [SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollution](https://mlsanity.com/paper/1805.00889/) (2018)

## Built on by (most cited first, 4 of 4)

- [Dual Knowledge Distillation for Efficient Sound Event Detection](https://mlsanity.com/paper/2402.02781/) (2024)
- [A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes](https://mlsanity.com/paper/2202.01487/) (2022)
- [Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification](https://mlsanity.com/paper/2504.03329/) (2025)
- [Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification](https://mlsanity.com/paper/2607.13571/) (2026)

## Abstract (arXiv)

Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes. Until recently only target sound events were considered when synthesizing the soundscapes. However, recorded soundscapes often contain a substantial amount of non-target events that may affect the performance. In this paper, we focus on the impact of these non-target events in the synthetic soundscapes. Firstly, we investigate to what extent using non-target events alternatively during the training or validation phase (or none of them) helps the system to correctly detect target events. Secondly, we analyze to what extend adjusting the signal-to-noise ratio between target and non-target events at training improves the sound event detection performance. The results show that using both target and non-target events for only one of the phases (validation or training) helps the system to properly detect sound events, outperforming the baseline (which uses non-target events in both phases). The paper also reports the results of a preliminary study on evaluating the system on clips that contain only non-target events. This opens questions for future work on non-target subset and acoustic similarity between target and non-target events which might confuse the system.