  In a typical sound event detection (SED) system, the existence of a sound
event is detected at a frame level, and consecutive frames with the same event
detected are combined as one sound event. The median filter is applied as a
post-processing step to remove detection errors as much as possible. However,
detection errors occurring around the onset and offset of a sound event are
beyond the capacity of the median filter. To address this issue, an onset and
offset weighted binary cross-entropy (OWBCE) loss function is proposed in this
paper, which trains the DNN model to be more robust on frames around (a) onsets
and offsets. Experiments are carried out in the context of DCASE 2022 task 4.
Results show that OWBCE outperforms BCE when different models are considered.
For a basic CRNN, relative improvements of 6.43% in event-F1, 1.96% in PSDS1,
and 2.43% in PSDS2 can be achieved by OWBCE.
