---
_id: '12890'
abstract:
- lang: eng
  text: 'We formulate a generic framework for blind source separation (BSS), which
    allows integrating data-driven spectro-temporal methods, such as deep clustering
    and deep attractor networks, with physically motivated probabilistic spatial methods,
    such as complex angular central Gaussian mixture models. The integrated model
    exploits the complementary strengths of the two approaches to BSS: the strong
    modeling power of neural networks, which, however, is based on supervised learning,
    and the ease of unsupervised learning of the spatial mixture models whose few
    parameters can be estimated on as little as a single segment of a real mixture
    of speech. Experiments are carried out on both artificially mixed speech and true
    recordings of speech mixtures. The experiments verify that the integrated models
    consistently outperform the individual components. We further extend the models
    to cope with noisy, reverberant speech and introduce a cross-domain teacher–student
    training where the mixture model serves as the teacher to provide training targets
    for the student neural network.'
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Drude L, Haeb-Umbach R. Integration of Neural Networks and Probabilistic Spatial
    Models for Acoustic Blind Source Separation. <i>IEEE Journal of Selected Topics
    in Signal Processing</i>. 2019. doi:<a href="https://doi.org/10.1109/JSTSP.2019.2912565">10.1109/JSTSP.2019.2912565</a>
  apa: Drude, L., &#38; Haeb-Umbach, R. (2019). Integration of Neural Networks and
    Probabilistic Spatial Models for Acoustic Blind Source Separation. <i>IEEE Journal
    of Selected Topics in Signal Processing</i>. <a href="https://doi.org/10.1109/JSTSP.2019.2912565">https://doi.org/10.1109/JSTSP.2019.2912565</a>
  bibtex: '@article{Drude_Haeb-Umbach_2019, title={Integration of Neural Networks
    and Probabilistic Spatial Models for Acoustic Blind Source Separation}, DOI={<a
    href="https://doi.org/10.1109/JSTSP.2019.2912565">10.1109/JSTSP.2019.2912565</a>},
    journal={IEEE Journal of Selected Topics in Signal Processing}, author={Drude,
    Lukas and Haeb-Umbach, Reinhold}, year={2019} }'
  chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks
    and Probabilistic Spatial Models for Acoustic Blind Source Separation.” <i>IEEE
    Journal of Selected Topics in Signal Processing</i>, 2019. <a href="https://doi.org/10.1109/JSTSP.2019.2912565">https://doi.org/10.1109/JSTSP.2019.2912565</a>.
  ieee: L. Drude and R. Haeb-Umbach, “Integration of Neural Networks and Probabilistic
    Spatial Models for Acoustic Blind Source Separation,” <i>IEEE Journal of Selected
    Topics in Signal Processing</i>, 2019.
  mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and
    Probabilistic Spatial Models for Acoustic Blind Source Separation.” <i>IEEE Journal
    of Selected Topics in Signal Processing</i>, 2019, doi:<a href="https://doi.org/10.1109/JSTSP.2019.2912565">10.1109/JSTSP.2019.2912565</a>.
  short: L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing
    (2019).
date_created: 2019-07-26T08:38:46Z
date_updated: 2022-01-06T06:51:23Z
ddc:
- '050'
department:
- _id: '54'
doi: 10.1109/JSTSP.2019.2912565
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2019-08-07T07:12:21Z
  date_updated: 2019-08-14T07:11:22Z
  file_id: '12903'
  file_name: IEEE Jounal_2019_Drude_Paper.pdf
  file_size: 967424
  relation: main_file
file_date_updated: 2019-08-14T07:11:22Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Journal of Selected Topics in Signal Processing
publication_identifier:
  eissn:
  - 1941-0484
status: public
title: Integration of Neural Networks and Probabilistic Spatial Models for Acoustic
  Blind Source Separation
type: journal_article
user_id: '11213'
year: '2019'
...