---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Drude, Lukas
      foaf_surname: Drude
      foaf_workInfoHomepage: http://www.librecat.org/personId=11213
  - foaf_Person:
      foaf_givenName: Thilo
      foaf_name: von Neumann, Thilo
      foaf_surname: von Neumann
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2018^xs_gYear
  dct_language: eng
  dct_title: Deep Attractor Networks for Speaker Re-Identifikation and Blind Source
    Separation@
...