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

KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing