# Improving Sound Event Detection In Domestic Environments Using Sound Separation

arXiv: https://arxiv.org/abs/2007.03932 · HTML: https://mlsanity.com/paper/2007.03932/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Self-Supervised Learning from Automatically Separated Sound Scenes](https://mlsanity.com/paper/2105.02132/) (2021)
- [Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience](https://mlsanity.com/paper/2402.03710/) (2024)
- [Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation](https://mlsanity.com/paper/2306.10240/) (2023)
- [Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection](https://mlsanity.com/paper/2103.12388/) (2021)
- [A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes](https://mlsanity.com/paper/2202.01487/) (2022)
- [Complete and separate: Conditional separation with missing target source attribute completion](https://mlsanity.com/paper/2307.14609/) (2023)

## Abstract (arXiv)

Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or noise. Until now these problems were mainly tackled at the classifier level. We propose to use sound separation as a pre-processing for sound event detection. In this paper we start from a sound separation model trained on the Free Universal Sound Separation dataset and the DCASE 2020 task 4 sound event detection baseline. We explore different methods to combine separated sound sources and the original mixture within the sound event detection. Furthermore, we investigate the impact of adapting the sound separation model to the sound event detection data on both the sound separation and the sound event detection.