Papers › SubSpectral Normalization for Neural Audio Data Processing
SubSpectral Normalization for Neural Audio Data Processing
Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keyword Spotting | Google Speech Commands | res8 w/ SSN(S=4, A=Sub) | % Test Accuracy | 95.4% ±0.22 | #40 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | res15 w/ SSN(S=4, A=Sub) | % Test Accuracy | 96.8% ±0.13 | #41 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | res15 w/ SSN(S=4, A=Sub) (2019) | % Test Accuracy | 97.5% ±0.15 | #42 of 42 | Archive leaderboard | report |
| Keyword Spotting | TAU Urban Acoustic Scenes 2019 | CP-ResNet(ch64) w/ SSN(S=2, A=Sub) | Accuracy | 83.6% ±0.07 | #1 of 2 | Archive leaderboard | report |
| Keyword Spotting | TAU Urban Acoustic Scenes 2019 | CP-ResNet(ch128) w/ SSN(S=2, A=Sub) | Accuracy | 84.1% ±0.20 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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