  Deep neural network based methods have been successfully applied to music
source separation. They typically learn a mapping from a mixture spectrogram to
a set of source spectrograms, all with magnitudes only. This approach has
several limitations: 1) its incorrect phase reconstruction degrades the
performance, 2) it limits the magnitude of masks between 0 and 1 while we
observe that 22% of time-frequency bins have ideal ratio mask values of over~1
in a popular dataset, MUSDB18, 3) its potential on very deep architectures is
under-explored. Our proposed system is designed to overcome these. First, we
propose to estimate phases by estimating complex ideal ratio masks (cIRMs)
where we decouple the estimation of cIRMs into magnitude and phase estimations.
Second, we extend the separation method to effectively allow the magnitude of
the mask to be larger than 1. Finally, we propose a residual UNet architecture
with up to 143 layers. Our proposed system achieves a state-of-the-art MSS
result on the MUSDB18 dataset, especially, a SDR of 8.98~dB on vocals,
outperforming the previous best performance of 7.24~dB. The source code is
available at: https://github.com/bytedance/music_source_separation
