  In this paper, we describe in detail our system for DCASE 2022 Task4. The
system combines two considerably different models: an end-to-end Sound Event
Detection Transformer (SEDT) and a frame-wise model, Metric Learning and Focal
Loss CNN (MLFL-CNN). The former is an event-wise model which learns event-level
representations and predicts sound event categories and boundaries directly,
while the latter is based on the widely adopted frame-classification scheme,
under which each frame is classified into event categories and event boundaries
are obtained by post-processing such as thresholding and smoothing. For SEDT,
self-supervised pre-training using unlabeled data is applied, and
semi-supervised learning is adopted by using an online teacher, which is
updated from the student model using the Exponential Moving Average (EMA)
strategy and generates reliable pseudo labels for weakly-labeled and unlabeled
data. For the frame-wise model, the ICT-TOSHIBA system of DCASE 2021 Task 4 is
used. Experimental results show that the hybrid system considerably outperforms
either individual model and achieves psds1 of 0.420 and psds2 of 0.783 on the
validation set without external data. The code is available at
https://github.com/965694547/Hybrid-system-of-frame-wise-model-and-SEDT.
