  Sound event detection is a core module for acoustic environmental analysis.
Semi-supervised learning technique allows to largely scale up the dataset
without increasing the annotation budget, and recently attracts lots of
research attention. In this work, we study on two advanced semi-supervised
learning techniques for sound event detection. Data augmentation is important
for the success of recent deep learning systems. This work studies the
audio-signal random augmentation method, which provides an augmentation
strategy that can handle a large number of different audio transformations. In
addition, consistency regularization is widely adopted in recent
state-of-the-art semi-supervised learning methods, which exploits the
unlabelled data by constraining the prediction of different transformations of
one sample to be identical to the prediction of this sample. This work finds
that, for semi-supervised sound event detection, consistency regularization is
an effective strategy, especially the best performance is achieved when it is
combined with the MeanTeacher model.
