Joint Weakly Supervised AT and AED Using Deep Feature Distillation and Adaptive Focal Loss

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.