  Learning meaningful frame-wise features on a partially labeled dataset is
crucial to semi-supervised sound event detection. Prior works either maintain
consistency on frame-level predictions or seek feature-level similarity among
neighboring frames, which cannot exploit the potential of unlabeled data. In
this work, we design a Local and Global Consistency (LGC) regularization scheme
to enhance the model on both label- and feature-level. The audio CutMix is
introduced to change the contextual information of clips. Then, the local
consistency is adopted to encourage the model to leverage local features for
frame-level predictions, and the global consistency is applied to force
features to align with global prototypes through a specially designed
contrastive loss. Experiments on the DESED dataset indicate the superiority of
LGC, surpassing its respective competitors largely with the same settings as
the baseline system. Besides, combining LGC with existing methods can obtain
further improvements. The code will be released soon.
