  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.
