  Recently, many methods based on deep learning have been proposed for music
source separation. Some state-of-the-art methods have shown that stacking many
layers with many skip connections improve the SDR performance. Although such a
deep and complex architecture shows outstanding performance, it usually
requires numerous computing resources and time for training and evaluation.
This paper proposes a two-stream neural network for music demixing, called
KUIELab-MDX-Net, which shows a good balance of performance and required
resources. The proposed model has a time-frequency branch and a time-domain
branch, where each branch separates stems, respectively. It blends results from
two streams to generate the final estimation. KUIELab-MDX-Net took second place
on leaderboard A and third place on leaderboard B in the Music Demixing
Challenge at ISMIR 2021. This paper also summarizes experimental results on
another benchmark, MUSDB18. Our source code is available online.

Audio and Speech Processing;Sound