  Large-scale audio tagging datasets inevitably contain imperfect labels, such
as clip-wise annotated (temporally weak) tags with no exact on- and offsets,
due to a high manual labeling cost. This work proposes pseudo strong labels
(PSL), a simple label augmentation framework that enhances the supervision
quality for large-scale weakly supervised audio tagging. A machine annotator is
first trained on a large weakly supervised dataset, which then provides finer
supervision for a student model. Using PSL we achieve an mAP of 35.95 balanced
train subset of Audioset using a MobileNetV2 back-end, significantly
outperforming approaches without PSL. An analysis is provided which reveals
that PSL mitigates missing labels. Lastly, we show that models trained with PSL
are also superior at generalizing to the Freesound datasets (FSD) than their
weakly trained counterparts.
