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Music Source Separation PyTorch Checkpoints

descriptionPublicationkeyboard_double_arrow_right Article 12 Sep 2021 English Publisher:Zenodo
Authors: Qiuqiang Kong; Cao, Yin; Haohe Liu; Keunwoo Choi; Yuxuan Wang;

doi: 10.5281/zenodo.5506761 , 10.5281/zenodo.5804160 , 10.5281/zenodo.5507029 , 10.5281/zenodo.5799080 , 10.5281/zenodo.5513378 , 10.5281/zenodo.5804141 , 10.5281/zenodo.5501678 , 10.5281/zenodo.5501677

Music Source Separation PyTorch Checkpoints

Abstract

Checkpoints of music source separation system. Users can separate vocals and accompaniment by using these checkpoints. See the GitHub repo for details: https://github.com/bytedance/music_source_separation Reference [1] Qiuqiang Kong, Yin Cao, Haohe Liu, Keunwoo Choi, Yuxuan Wang, Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation, ISMIR2021.

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music source separation

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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