  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.
