  While many deep learning methods on other domains have been applied to sound
event detection (SED), differences between original domains of the methods and
SED have not been appropriately considered so far. As SED uses audio data with
two dimensions (time and frequency) for input, thorough comprehension on these
two dimensions is essential for application of methods from other domains on
SED. Previous works proved that methods those address on frequency dimension
are especially powerful in SED. By applying FilterAugment and frequency dynamic
convolution those are frequency dependent methods proposed to enhance SED
performance, our submitted models achieved best PSDS1 of 0.4704 and best PSDS2
of 0.8224.
