  Sound event detection (SED), as a core module of acoustic environmental
analysis, suffers from the problem of data deficiency. The integration of
semi-supervised learning (SSL) largely mitigates such problem while bringing no
extra annotation budget. This paper researches on several core modules of SSL,
and introduces a random consistency training (RCT) strategy. First, a
self-consistency loss is proposed to fuse with the teacher-student model to
stabilize the training. Second, a hard mixup data augmentation is proposed to
account for the additive property of sounds. Third, a random augmentation
scheme is applied to flexibly combine different types of data augmentations.
Experiments show that the proposed strategy outperform other widely-used
strategies.
