The impact of non-target events in synthetic soundscapes for sound event detection
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 28 Sep 2021.
Builds on
8 of 21 references.
References · 8
- 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 339
- 2021The Benefit Of Temporally-Strong Labels In Audio Event Classificationcited by 83
- 2019A Framework for the Robust Evaluation of Sound Event Detectioncited by 28
- 2020What's All the FUSS About Free Universal Sound Separation Data?cited by 22
- 2018Adaptive pooling operators for weakly labeled sound event detectioncited by 21
- 2018A Closer Look at Weak Label Learning for Audio Eventscited by 11
- 2018Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environmentscited by 10
- 2018SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollutioncited by 5
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2024Dual Knowledge Distillation for Efficient Sound Event Detectioncited by 2
- 2022A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapescited by 1
- 2025Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classificationcited by 1
- 2026Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classificationcited by 0
- 2026Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification15 Jul 2026
- 2025Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification4 Apr 2025
- 2024Dual Knowledge Distillation for Efficient Sound Event Detection5 Feb 2024
- 2022A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes3 Feb 2022