Frequency-aware convolution for sound event detection

In sound event detection (SED), convolution neural networks (CNNs) are widely used to extract time-frequency patterns from the input spectrogram.

However, features extracted by CNN can be insensitive to the shift of time-frequency patterns along the frequency axis.

To address this issue, frequency dynamic convolution (FDY) has been proposed, which applies different kernels to different frequency components.

Compared to the vannila CNN, FDY requires several times more parameters.

In this paper, a more efficient solution named frequency-aware convolution (FAC) is proposed.

In FAC, frequency-positional information is encoded in a vector and added to the input spectrogram.

To match the amplitude of input, the encoding vector is scaled adaptively and channel-independently.

Experiments are carried out in the context of DCASE 2022 task 4, and the results demonstrate that FAC can achieve comparable performance to that of FDY with only 515 additional parameters, while FDY requires 8.02 million additional parameters.

The ablation study shows that scaling the encoding vector adaptively and channel-independently is critical to the performance of FAC.