2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-7.1
Paper Title LASAFT: LATENT SOURCE ATTENTIVE FREQUENCY TRANSFORMATION FOR CONDITIONED SOURCE SEPARATION
Authors Woosung Choi, Minseok Kim, Korea University, South Korea; Jaehwa Chung, Korea National Open University, South Korea; Soonyoung Jung, Korea University, South Korea
SessionAUD-7: Audio and Speech Source Separation 3: Deep Learning
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SEP] Audio and Speech Source Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.