  In this paper, we present a method called HODGEPODGE\footnotemark[1] for
large-scale detection of sound events using weakly labeled, synthetic, and
unlabeled data proposed in the Detection and Classification of Acoustic Scenes
and Events (DCASE) 2019 challenge Task 4: Sound event detection in domestic
environments. To perform this task, we adopted the convolutional recurrent
neural networks (CRNN) as our backbone network. In order to deal with a small
amount of tagged data and a large amounts of unlabeled in-domain data, we aim
to focus primarily on how to apply semi-supervise learning methods efficiently
to make full use of limited data. Three semi-supervised learning principles
have been used in our system, including: 1) Consistency regularization applies
data augmentation; 2) MixUp regularizer requiring that the predictions for a
interpolation of two inputs is close to the interpolation of the prediction for
each individual input; 3) MixUp regularization applies to interpolation between
data augmentations. We also tried an ensemble of various models, which are
trained by using different semi-supervised learning principles. Our proposed
approach significantly improved the performance of the baseline, achieving the
event-based f-measure of 42.0\% compared to 25.8\% event-based f-measure of the
baseline in the provided official evaluation dataset. Our submissions ranked
third among 18 teams in the task 4.
