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Recent deep-learning approaches have shown that Frequency Transformation (FT)blocks can significantly improve spectrogram-based single-source separationmodels by capturing frequency patterns. The goal of this paper is to extend theFT block to fit the multi-source task. We propose the Latent Source AttentiveFrequency Transformation (LaSAFT) block to capture source-dependent frequencypatterns. We also propose the Gated Point-wise Convolutional Modulation(GPoCM), an extension of Feature-wise Linear Modulation (FiLM), to modulateinternal features. By employing these two novel methods, we extend theConditioned-U-Net (CUNet) for multi-source separation, and the experimentalresults indicate that our LaSAFT and GPoCM can improve the CUNet's performance,achieving state-of-the-art SDR performance on several MUSDB18 source separationtasks.

LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation