  Frequency dynamic convolution (FDY conv) has shown the state-of-the-art
performance in sound event detection (SED) using frequency-adaptive kernels
obtained by frequency-varying combination of basis kernels. However, FDY conv
lacks an explicit mean to diversify frequency-adaptive kernels, potentially
limiting the performance. In addition, size of basis kernels is limited while
time-frequency patterns span larger spectro-temporal range. Therefore, we
propose dilated frequency dynamic convolution (DFD conv) which diversifies and
expands frequency-adaptive kernels by introducing different dilation sizes to
basis kernels. Experiments showed advantages of varying dilation sizes along
frequency dimension, and analysis on attention weight variance proved dilated
basis kernels are effectively diversified. By adapting class-wise median filter
with intersection-based F1 score, proposed DFD-CRNN outperforms FDY-CRNN by
3.12% in terms of polyphonic sound detection score (PSDS).
