  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 of DCASE2019 Task 4 for both AT and AED tasks. A
frame-level target-events based deep feature distillation is first proposed, it
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, in which the contribution of
difficult-to-classify and easy-to-classify 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 DCASE2019 Task4 dataset, and
results show that our approach achieves competitive performances in both AT
(49.8% F1-score) and AED (81.2% F1-score) tasks.
