  A good joint training framework is very helpful to improve the performances
of weakly supervised audio tagging (AT) and acoustic event detection (AED)
simultaneously. In this study, we propose three methods to improve the best
teacher-student framework in the IEEE AASP Challenge on Detection and
Classification of Acoustic Scenes and Events (DCASE) 2019 Task 4 for both audio
tagging and acoustic events detection tasks. A frame-level target-events based
deep feature distillation is first proposed, which aims to leverage the
potential of limited strong-labeled data in weakly supervised framework to
learn better intermediate feature maps. Then, we propose an adaptive focal loss
and two-stage training strategy to enable an effective and more accurate model
training, where the contribution of hard and easy acoustic events to the total
cost function can be automatically adjusted. Furthermore, an event-specific
post processing is designed to improve the prediction of target event
time-stamps. Our experiments are performed on the public DCASE 2019 Task 4
dataset, results show that our approach achieves competitive performances in
both AT (81.2\% F1-score) and AED (49.8\% F1-score) tasks.
