  We describe a novel weakly labeled Audio Event Classification approach based
on a self-supervised attention model. The weakly labeled framework is used to
eliminate the need for expensive data labeling procedure and self-supervised
attention is deployed to help a model distinguish between relevant and
irrelevant parts of a weakly labeled audio clip in a more effective manner
compared to prior attention models. We also propose a highly effective strongly
supervised attention model when strong labels are available. This model also
serves as an upper bound for the self-supervised model. The performances of the
model with self-supervised attention training are comparable to the strongly
supervised one which is trained using strong labels. We show that our
self-supervised attention method is especially beneficial for short audio
events. We achieve 8.8% and 17.6% relative mean average precision improvements
over the current state-of-the-art systems for SL-DCASE-17 and balanced
AudioSet.
