  In this paper, we describe in detail the system we submitted to DCASE2019
task 4: sound event detection (SED) in domestic environments. We employ a
convolutional neural network (CNN) with an embedding-level attention pooling
module to solve it. By considering the interference caused by the co-occurrence
of multiple events in the unbalanced dataset, we utilize the disentangled
feature to raise the performance of the model. To take advantage of the
unlabeled data, we adopt Guided Learning for semi-supervised learning. A group
of median filters with adaptive window sizes is utilized in the post-processing
of output probabilities of the model. We also analyze the effect of the
synthetic data on the performance of the model and finally achieve an
event-based F-measure of 45.43% on the validation set and an event-based
F-measure of 42.7% on the test set. The system we submitted to the challenge
achieves the best performance compared to those of other participates.
