  Due to the limitation of strong-labeled sound event detection data set, using
synthetic data to improve the sound event detection system performance has been
a new research focus. In this paper, we try to exploit the usage of synthetic
data to improve the feature representation. Based on metric learning, we
proposed inter-frame distance loss function for domain adaptation, and prove
the effectiveness of it on sound event detection. We also applied multi-task
learning with synthetic data. We find the the best performance can be achieved
when the two methods being used together. The experiment on DCASE 2018 task 4
test set and DCASE 2019 task 4 synthetic set both show competitive results.
