  Convolutional Neural Networks are widely used in various machine learning
domains. In image processing, the features can be obtained by applying 2D
convolution to all spatial dimensions of the input. However, in the audio case,
frequency domain input like Mel-Spectrogram has different and unique
characteristics in the frequency dimension. Thus, there is a need for a method
that allows the 2D convolution layer to handle the frequency dimension
differently. In this work, we introduce SubSpectral Normalization (SSN), which
splits the input frequency dimension into several groups (sub-bands) and
performs a different normalization for each group. SSN also includes an affine
transformation that can be applied to each group. Our method removes the
inter-frequency deflection while the network learns a frequency-aware
characteristic. In the experiments with audio data, we observed that SSN can
efficiently improve the network's performance.
