---
_id: '12900'
abstract:
- lang: eng
  text: 'Deep attractor networks (DANs) are a recently introduced method to blindly
    separate sources from spectral features of a monaural recording using bidirectional
    long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is
    inherently not online-ready and resorting to operating on blocks yields a block
    permutation problem in that the index of each speaker may change between blocks.
    We here propose the joint modeling of spatial and spectral features to solve the
    block permutation problem and generalize DANs to multi-channel meeting recordings:
    The DAN acts as a spectral feature extractor for a subsequent model-based clustering
    approach. We first analyze different joint models in batch-processing scenarios
    and finally propose a block-online blind source separation algorithm. The efficacy
    of the proposed models is demonstrated on reverberant mixtures corrupted by real
    recordings of multi-channel background noise. We demonstrate that both the proposed
    batch-processing and the proposed block-online system outperform (a) a spatial-only
    model with a state-of-the-art frequency permutation solver and (b) a spectral-only
    model with an oracle block permutation solver in terms of signal to distortion
    ratio (SDR) gains.'
author:
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: ' Takuya '
  full_name: 'Higuchi,,  Takuya '
  last_name: Higuchi,
- first_name: 'Keisuke '
  full_name: 'Kinoshita, Keisuke '
  last_name: Kinoshita
- first_name: 'Tomohiro '
  full_name: 'Nakatani, Tomohiro '
  last_name: Nakatani
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Drude L, Higuchi,  Takuya , Kinoshita K, Nakatani T, Haeb-Umbach R. Dual Frequency-
    and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source
    Separation. In: <i>ICASSP 2018, Calgary, Canada</i>. ; 2018.'
  apa: Drude, L., Higuchi,  Takuya , Kinoshita, K., Nakatani, T., &#38; Haeb-Umbach,
    R. (2018). Dual Frequency- and Block-Permutation Alignment for Deep Learning Based
    Block-Online Blind Source Separation. In <i>ICASSP 2018, Calgary, Canada</i>.
  bibtex: '@inproceedings{Drude_Higuchi,_Kinoshita_Nakatani_Haeb-Umbach_2018, title={Dual
    Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
    Blind Source Separation}, booktitle={ICASSP 2018, Calgary, Canada}, author={Drude,
    Lukas and Higuchi,  Takuya  and Kinoshita, Keisuke  and Nakatani, Tomohiro  and
    Haeb-Umbach, Reinhold}, year={2018} }'
  chicago: Drude, Lukas,  Takuya  Higuchi, Keisuke  Kinoshita, Tomohiro  Nakatani,
    and Reinhold Haeb-Umbach. “Dual Frequency- and Block-Permutation Alignment for
    Deep Learning Based Block-Online Blind Source Separation.” In <i>ICASSP 2018,
    Calgary, Canada</i>, 2018.
  ieee: L. Drude,  Takuya  Higuchi, K. Kinoshita, T. Nakatani, and R. Haeb-Umbach,
    “Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
    Blind Source Separation,” in <i>ICASSP 2018, Calgary, Canada</i>, 2018.
  mla: Drude, Lukas, et al. “Dual Frequency- and Block-Permutation Alignment for Deep
    Learning Based Block-Online Blind Source Separation.” <i>ICASSP 2018, Calgary,
    Canada</i>, 2018.
  short: 'L. Drude,  Takuya  Higuchi, K. Kinoshita, T. Nakatani, R. Haeb-Umbach, in:
    ICASSP 2018, Calgary, Canada, 2018.'
date_created: 2019-07-30T14:42:15Z
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_Drude_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Poster.pdf
status: public
title: Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
  Blind Source Separation
type: conference
user_id: '44006'
year: '2018'
...