---
_id: '12898'
abstract:
- lang: eng
  text: Deep clustering (DC) and deep attractor networks (DANs) are a data-driven
    way to monaural blind source separation. Both approaches provide astonishing single
    channel performance but have not yet been generalized to block-online processing.
    When separating speech in a continuous stream with a block-online algorithm, it
    needs to be determined in each block which of the output streams belongs to whom.
    In this contribution we solve this block permutation problem by introducing an
    additional speaker identification embedding to the DAN model structure. We motivate
    this model decision by analyzing the embedding topology of DC and DANs and show,
    that DC and DANs themselves are not sufficient for speaker identification. This
    model structure (a) improves the signal to distortion ratio (SDR) over a DAN baseline
    and (b) provides up to 61% and up to 34% relative reduction in permutation error
    rate and re-identification error rate compared to an i-vector baseline, respectively.
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Thilo
  full_name: von Neumann, Thilo
  last_name: von Neumann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, von Neumann T, Haeb-Umbach R. Deep Attractor Networks for Speaker
    Re-Identifikation and Blind Source Separation. In: <i>ICASSP 2018, Calgary, Canada</i>.
    ; 2018.'
  apa: Drude, L., von Neumann, T., &#38; Haeb-Umbach, R. (2018). Deep Attractor Networks
    for Speaker Re-Identifikation and Blind Source Separation. In <i>ICASSP 2018,
    Calgary, Canada</i>.
  bibtex: '@inproceedings{Drude_von Neumann_Haeb-Umbach_2018, title={Deep Attractor
    Networks for Speaker Re-Identifikation and Blind Source Separation}, booktitle={ICASSP
    2018, Calgary, Canada}, author={Drude, Lukas and von Neumann, Thilo and Haeb-Umbach,
    Reinhold}, year={2018} }'
  chicago: Drude, Lukas, Thilo von Neumann, and Reinhold Haeb-Umbach. “Deep Attractor
    Networks for Speaker Re-Identifikation and Blind Source Separation.” In <i>ICASSP
    2018, Calgary, Canada</i>, 2018.
  ieee: L. Drude, T. von Neumann, and R. Haeb-Umbach, “Deep Attractor Networks for
    Speaker Re-Identifikation and Blind Source Separation,” in <i>ICASSP 2018, Calgary,
    Canada</i>, 2018.
  mla: Drude, Lukas, et al. “Deep Attractor Networks for Speaker Re-Identifikation
    and Blind Source Separation.” <i>ICASSP 2018, Calgary, Canada</i>, 2018.
  short: 'L. Drude, T. von Neumann, R. Haeb-Umbach, in: ICASSP 2018, Calgary, Canada,
    2018.'
date_created: 2019-07-30T14:22:53Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude2_Slides.pdf
status: public
title: Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation
type: conference
user_id: '44006'
year: '2018'
...