  In this paper, we describe in detail our systems for DCASE 2020 Task 4. The
systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts
weakly-supervised framework with an attention-based embedding-level pooling
module and a semi-supervised learning approach named guided learning. This
year, we incorporate multi-branch learning (MBL) into the original system to
further improve its performance. MBL uses different branches with different
pooling strategies (including instance-level and embedding-level strategies)
and different pooling modules (including attention pooling, global max pooling
or global average pooling modules), which share the same feature encoder of the
model. Therefore, multiple branches pursuing different purposes and focusing on
different characteristics of the data can help the feature encoder model the
feature space better and avoid over-fitting. To better exploit the
strongly-labeled synthetic data, inspired by multi-task learning, we also
employ a sound event detection branch. To combine sound separation (SS) with
sound event detection (SED), we fuse the results of SED systems with SS-SED
systems which are trained using separated sound output by an SS system. The
experimental results prove that MBL can improve the model performance and using
SS has great potential to improve the performance of SED ensemble system.
