  Music source separation is important for applications such as karaoke and
remixing. Much of previous research focuses on estimating short-time Fourier
transform (STFT) magnitude and discarding phase information. We observe that,
for singing voice separation, phase can make considerable improvement in
separation quality. This paper proposes a complex ratio masking method for
voice and accompaniment separation. The proposed method employs DenseUNet with
self attention to estimate the real and imaginary components of STFT for each
sound source. A simple ensemble technique is introduced to further improve
separation performance. Evaluation results demonstrate that the proposed method
outperforms recent state-of-the-art models for both separated voice and
accompaniment.
