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

Minseok Kim; Woosung Choi; Jaehwa Chung; Daewon Lee; Soonyoung Jung

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arXiv · 2111.12203

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

Minseok Kim·Woosung Choi·Jaehwa Chung·Daewon Lee·Soonyoung Jung

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Abstract

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.

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Minseok Kim, Woosung Choi, Jaehwa Chung, Daewon Lee, Soonyoung Jung. 2021-11-24. KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing. https://arxiv.org/abs/2111.12203

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