---
res:
  bibo_abstract:
  - 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.@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: Daniel
      foaf_name: Hasenklever, Daniel
      foaf_surname: Hasenklever
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2019^xs_gYear
  dct_language: eng
  dct_title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind
    Source Separation@
...