# Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes

arXiv: https://arxiv.org/abs/2011.00801 · HTML: https://mlsanity.com/paper/2011.00801/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection](https://mlsanity.com/paper/2109.00962/) (2021)
- [Self-Supervised Learning from Automatically Separated Sound Scenes](https://mlsanity.com/paper/2105.02132/) (2021)
- [Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet](https://mlsanity.com/paper/2402.17701/) (2024)
- [Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4](https://mlsanity.com/paper/2107.02569/) (2021)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)
- [Couple Learning for semi-supervised sound event detection](https://mlsanity.com/paper/2110.05809/) (2021)
- [A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes](https://mlsanity.com/paper/2202.01487/) (2022)
- [Improving snore detection under limited dataset through harmonic/percussive source separation and convolutional neural networks](https://mlsanity.com/paper/2410.23796/) (2024)
- [REVE: Efficient Hallucination Correction for Large Audio-Language Models via Reused Encoder States](https://mlsanity.com/paper/2609.26028/) (2026)

## Abstract (arXiv)

We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2021 task 4 depending on time related modifications (time position of an event and length of clips) and we study the impact of non-target sound events and reverberation. We show that the localization in time of sound events is still a problem for SED systems. We also show that reverberation and non-target sound events are severely degrading the performance of the SED systems. In the latter case, sound separation seems like a promising solution.