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Convolutional neural networks with skip connections have shown goodperformance in music source separation. In this work, we propose a denoisingAuto-encoder with Recurrent skip Connections (ARC). We use 1D convolution alongthe temporal axis of the time-frequency feature map in all layers of thefully-convolutional network. The use of 1D convolution makes it possible toapply recurrent layers to the intermediate outputs of the convolution layers.In addition, we also propose an enhancement network and a residual regressionmethod to further improve the separation result. The recurrent skipconnections, the enhancement module, and the residual regression all improvethe separation quality. The ARC model with residual regression achieves 5.74siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We alsoevaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016)and it achieves 5.91 SDR in vocals.

Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation