LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation

• URL: http://arxiv.org/abs/2010.11631v2
• Date: Wed, 14 Apr 2021 05:31:12 GMT
• Title: LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
• Authors: Woosung Choi and Minseok Kim and Jaehwa Chung and Soonyoung Jung
• Abstract summary: 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) to modulate internal features.
• Score: 7.002478301291264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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