  There are two sub-tasks implied in the weakly-supervised SED: audio tagging
and event boundary detection. Current methods which combine multi-task learning
with SED requires annotations both for these two sub-tasks. Since there are
only annotations for audio tagging available in weakly-supervised SED, we
design multiple branches with different learning purposes instead of pursuing
multiple tasks. Similar to multiple tasks, multiple different learning purposes
can also prevent the common feature which the multiple branches share from
overfitting to any one of the learning purposes. We design these multiple
different learning purposes based on combinations of different MIL strategies
and different pooling methods. Experiments on the DCASE 2018 Task 4 dataset and
the URBAN-SED dataset both show that our method achieves competitive
performance.
