  In recent years, exploring effective sound separation (SSep) techniques to
improve overlapping sound event detection (SED) attracts more and more
attention. Creating accurate separation signals to avoid the catastrophic error
accumulation during SED model training is very important and challenging. In
this study, we first propose a novel selective pseudo-labeling approach, termed
SPL, to produce high confidence separated target events from blind sound
separation outputs. These target events are then used to fine-tune the original
SED model that pre-trained on the sound mixtures in a multi-objective learning
style. Then, to further leverage the SSep outputs, a class-wise discriminative
fusion is proposed to improve the final SED performances, by combining multiple
frame-level event predictions of both sound mixtures and their separated
signals. All experiments are performed on the public DCASE 2021 Task 4 dataset,
and results show that our approaches significantly outperforms the official
baseline, the collar-based F 1, PSDS1 and PSDS2 performances are improved from
44.3%, 37.3% and 54.9% to 46.5%, 44.5% and 75.4%, respectively.
