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Singing Voice Separation (SVS) tries to separate singing voice from a givenmixed musical signal. Recently, many U-Net-based models have been proposed forthe SVS task, but there were no existing works that evaluate and comparevarious types of intermediate blocks that can be used in the U-Netarchitecture. In this paper, we introduce a variety of intermediate spectrogramtransformation blocks. We implement U-nets based on these blocks and train themon complex-valued spectrograms to consider both magnitude and phase. Thesenetworks are then compared on the SDR metric. When using a particular blockcomposed of convolutional and fully-connected layers, it achievesstate-of-the-art SDR on the MUSDB singing voice separation task by a largemargin of 0.9 dB. Our code and models are available online.

Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation