road2018 / unsupervised_spatial_dc

Code for the paper: "Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information"

unsupervised_spatial_dc

Code for the paper: "Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information"

Please cite as:

@INPROCEEDINGS{8683201,
    author={E. {Tzinis} and S. {Venkataramani} and P. {Smaragdis}},
    booktitle={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    title={Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures Using Spatial Information},
    year={2019},
    volume={},
    number={},
    pages={81-85},
    keywords={pattern clustering;source separation;unsupervised learning;training process;ground truth separation information;direct learning;spatial information;monophonic source separation system;multichannel mixtures;unsupervised deep clustering approach;sound separation performance;multichannel recordings;Deep clustering;source separation;unsupervised learning},
    doi={10.1109/ICASSP.2019.8683201},
    ISSN={},
    month={May},}

Disclaimer

University of Illinois Open Source License

Copyright © 2018, University of Illinois at Urbana Champaign. All rights reserved.

Developed by: Efthymios Tzinis 1, Shrikant Venkataramani 1, Paris Smaragdis 1,2

1: University of Illinois at Urbana-Champaign, 2: Adobe Research

This work was supported by NSF grant 1453104. Paper link: https://doi.org/10.1109/ICASSP.2019.8683201

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