A general two-step training recipe for sound source separation. In the first step the ideal masks are learned under a front end transformation. The ideal masks or targets seve as an upper bound for source separation performance. Then we train the parameters of the separation module using SI-SDR loss on the trained latent targets. The corresponding paper has been submitted to ICASSP 2020.
University of Illinois Open Source License
Copyright © 2019, University of Illinois at Urbana Champaign. All rights reserved.
Developed by: Efthymios Tzinis 1, Shrikant Venkataramani 1, Zhepei Wang 1, Cem Subakan 2 and Paris Smaragdis 1,3
1: University of Illinois at Urbana-Champaign
2: Mila--Quebec Artificial Intelligence Institute
3: Adobe Research
This work was supported by NSF grant 1453104.
Paper link: https://arxiv.org/abs/1910.09804
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