---
_id: '12874'
abstract:
- lang: eng
  text: We propose a training scheme to train neural network-based source separation
    algorithms from scratch when parallel clean data is unavailable. In particular,
    we demonstrate that an unsupervised spatial clustering algorithm is sufficient
    to guide the training of a deep clustering system. We argue that previous work
    on deep clustering requires strong supervision and elaborate on why this is a
    limitation. We demonstrate that (a) the single-channel deep clustering system
    trained according to the proposed scheme alone is able to achieve a similar performance
    as the multi-channel teacher in terms of word error rates and (b) initializing
    the spatial clustering approach with the deep clustering result yields a relative
    word error rate reduction of 26% over the unsupervised teacher.
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Daniel
  full_name: Hasenklever, Daniel
  last_name: Hasenklever
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering
    Model for Multichannel Blind Source Separation. In: <i>ICASSP 2019, Brighton,
    UK</i>. ; 2019.'
  apa: Drude, L., Hasenklever, D., &#38; Haeb-Umbach, R. (2019). Unsupervised Training
    of a Deep Clustering Model for Multichannel Blind Source Separation. In <i>ICASSP
    2019, Brighton, UK</i>.
  bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised
    Training of a Deep Clustering Model for Multichannel Blind Source Separation},
    booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel
    and Haeb-Umbach, Reinhold}, year={2019} }'
  chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised
    Training of a Deep Clustering Model for Multichannel Blind Source Separation.”
    In <i>ICASSP 2019, Brighton, UK</i>, 2019.
  ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a
    Deep Clustering Model for Multichannel Blind Source Separation,” in <i>ICASSP
    2019, Brighton, UK</i>, 2019.
  mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for
    Multichannel Blind Source Separation.” <i>ICASSP 2019, Brighton, UK</i>, 2019.
  short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK,
    2019.'
date_created: 2019-07-23T07:37:54Z
date_updated: 2022-01-06T06:51:21Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2019-08-14T07:19:13Z
  date_updated: 2019-08-14T07:19:13Z
  file_id: '12925'
  file_name: ICASSP_2019_Drude_Paper.pdf
  file_size: 368225
  relation: main_file
file_date_updated: 2019-08-14T07:19:13Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source
  Separation
type: conference
user_id: '59789'
year: '2019'
...