  To tackle sound event detection (SED) task, we propose frequency dependent
networks (FreDNets), which heavily leverage frequency-dependent methods. We
apply frequency warping and FilterAugment, which are frequency-dependent data
augmentation methods. The model architecture consists of 3 branches: audio
teacher-student transformer (ATST) branch, BEATs branch and CNN branch
including either partial dilated frequency dynamic convolution (PDFD) or
squeeze-and-Excitation (SE) with time-frame frequency-wise SE (tfwSE). To train
MAESTRO labels with coarse temporal resolution, we apply max pooling on
prediction for the MAESTRO dataset. Using best ensemble model, we apply self
training to obtain pseudo label from DESED weak set, DESED unlabeled set and
AudioSet. AudioSet labels are filtered to focus on high-confidence pseudo
labels and AudioSet pseudo labels are used to train on DESED labels only. We
used change-detection-based sound event bounding boxes (cSEBBs) as post
processing for ensemble models on self training and submission models.
