/two_step_mask_learning

A two step optimization for sound source separation on the adaptive front-end domain

Primary LanguagePython

Two-Step Sound Source Separation: Training on Learned Latent Targets

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.

YouTube two-step separation presentation

Disclaimer

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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