  2D convolution is widely used in sound event detection (SED) to recognize two
dimensional time-frequency patterns of sound events. However, 2D convolution
enforces translation equivariance on sound events along both time and frequency
axis while frequency is not shift-invariant dimension. In order to improve
physical consistency of 2D convolution on SED, we propose frequency dynamic
convolution which applies kernel that adapts to frequency components of input.
Frequency dynamic convolution outperforms the baseline by 6.3% in DESED
validation dataset in terms of polyphonic sound detection score (PSDS). It also
significantly outperforms other pre-existing content-adaptive methods on SED.
In addition, by comparing class-wise F1 scores of baseline and frequency
dynamic convolution, we showed that frequency dynamic convolution is especially
more effective for detection of non-stationary sound events with intricate
time-frequency patterns. From this result, we verified that frequency dynamic
convolution is superior in recognizing frequency-dependent patterns.
