Papers › LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
Woosung Choi, Minseok Kim, Jaehwa Chung, Soonyoung Jung
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns. The goal of this paper is to extend the FT block to fit the multi-source task. We propose the Latent Source Attentive Frequency Transformation (LaSAFT) block to capture source-dependent frequency patterns. We also propose the Gated Point-wise Convolutional Modulation (GPoCM), an extension of Feature-wise Linear Modulation (FiLM), to modulate internal features. By employing these two novel methods, we extend the Conditioned-U-Net (CUNet) for multi-source separation, and the experimental results indicate that our LaSAFT and GPoCM can improve the CUNet's performance, achieving state-of-the-art SDR performance on several MUSDB18 source separation tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Music Source Separation | MUSDB18 | LaSAFT+GPoCM | SDR (avg) | 5.88 | #20 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | LaSAFT+GPoCM | SDR (bass) | 5.63 | #20 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | LaSAFT+GPoCM | SDR (drums) | 5.68 | #20 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | LaSAFT+GPoCM | SDR (other) | 4.87 | #20 of 27 | Archive leaderboard | report |
| Music Source Separation | MUSDB18 | LaSAFT+GPoCM | SDR (vocals) | 7.33 | #20 of 27 | 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.
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