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arXiv · 2010.11631

LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation

Woosung Choi·Minseok Kim·Jaehwa Chung·Soonyoung Jung
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Abstract

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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BibTeXRIS

Woosung Choi, Minseok Kim, Jaehwa Chung, Soonyoung Jung. 2020-10-22. LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation. https://arxiv.org/abs/2010.11631

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