# SP-SEDT: Self-supervised Pre-training for Sound Event Detection Transformer

arXiv: https://arxiv.org/abs/2111.15222 · HTML: https://mlsanity.com/paper/2111.15222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2021.

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

- [A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4](https://mlsanity.com/paper/2210.09529/) (2022)

## Abstract (arXiv)

Recently, an event-based end-to-end model (SEDT) has been proposed for sound event detection (SED) and achieves competitive performance. However, compared with the frame-based model, it requires more training data with temporal annotations to improve the localization ability. Synthetic data is an alternative, but it suffers from a great domain gap with real recordings. Inspired by the great success of UP-DETR in object detection, we propose to self-supervisedly pre-train SEDT (SP-SEDT) by detecting random patches (only cropped along the time axis). Experiments on the DCASE2019 task4 dataset show the proposed SP-SEDT can outperform fine-tuned frame-based model. The ablation study is also conducted to investigate the impact of different loss functions and patch size.