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Conditioned source separations have attracted significant attention becauseof their flexibility, applicability and extensionality. Their performance wasusually inferior to the existing approaches, such as the single sourceseparation model. However, a recently proposed method called LaSAFT-Net hasshown that conditioned models can show comparable performance against existingsingle-source separation models. This paper presents LightSAFT-Net, alightweight version of LaSAFT-Net. As a baseline, it provided a sufficient SDRperformance for comparison during the Music Demixing Challenge at ISMIR 2021.This paper also enhances the existing LightSAFT-Net by replacing the LightSAFTblocks in the encoder with TFC-TDF blocks. Our enhanced LightSAFT-Netoutperforms the previous one with fewer parameters.Conditioned sourceseparations have attracted significant attention because of their flexibility,applicability and extensionality. Their performance was usually inferior to theexisting approaches, such as the single source separation model. However, arecently proposed method called LaSAFT-Net has shown that conditioned modelscan show comparable performance against existing single-source separationmodels. This paper presents LightSAFT-Net, a lightweight version of LaSAFT-Net.As a baseline, it provided a sufficient SDR performance for comparison duringthe Music Demixing Challenge at ISMIR 2021.

LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation